D3 Data
The intelligence layer underneath the revenue system.
Every targeting decision downstream depends on what you actually know about the market. D3 Data maps it, identifies who decides, attaches observable signals, and scores opportunity so prioritization is made from evidence rather than instinct.
Beyond list building
A list says who exists. Intelligence says who is worth hours.
Any vendor can export ten thousand companies matching a filter. That export answers one question. It says nothing about which of those companies shows the fit, the signals, and the timing that predict a deal for your specific business.
D3 Data builds the picture per account and scores it against a model customized to what predicts revenue for you. The output is a ranked market your reps can act on Monday morning, and it feeds every other function: which accounts outbound touches, which get human hours, which segments the revenue plan is built around.
Because the model ingests your conversion data, the ranking gets more accurate the longer it runs. The accounts that close teach it what to look for next.
Factors a model can weigh
- ICP fit
- Company size
- Revenue estimate
- Industry
- Sales team size
- Technology in stack
- Hiring activity
- Leadership changes
- Growth markers
- Funding
- Google Ads activity
- Meta Ads activity
- Review data
- Website technology
- Social activity
- Custom buying signals
- Timing
- Potential ACV
- Reachability and data quality
Weights are customized per client. A signal that predicts deals for a payroll platform is noise for a logistics platform.
On data coverage
Coverage varies by market, geography, and company size. Some signals are reliably observable in one industry and absent in another. We report coverage per build before you rely on it, and we do not present an unverified field as a verified one or claim data no vendor actually has.
Total addressable market
24,382
accounts, enriched and scored
- Tier 13121.3%
Strong fit, live buying signals, right timing. Reps work these by hand.
- Tier 21,8467.6%
Good fit, weaker or no timing trigger. Covered by campaigns, promoted on signal.
- Tier 322,22491.1%
The long tail. Reached by email over time, monitored for change. No rep hours.
Bar lengths show rep attention, not account count: the 312 accounts at the top get most of the human hours, the 22,224 at the bottom get automated coverage. Figures are illustrative, not client data.
The process
Eight steps from a definition to a ranked market
Each step exists because skipping it shows up later as a bad connect rate, a bounced domain, or a rep calling an office with no budget authority.
- 01
Define the ICP
Vertical, geography, size, ownership structure, and the disqualification rules. Nothing gets built until the filters are written and approved.
- 02
Map the market
Build the universe of matching companies rather than filtering down a generic database that never indexed them properly.
- 03
Identify ownership
Find the person who actually decides. In owner-operated markets this single field is the difference between a conversation and a switchboard.
- 04
Verify what is verifiable
Validate contact data and mark what cannot be validated. An empty field is reported as empty rather than filled with a generic address.
- 05
Attach signals
Advertising activity, review volume and velocity, website technology, social posting, hiring, funding, and leadership change.
- 06
Score opportunity
Combine fit, size, signals, and timing into one number so the working list has an order rather than an alphabet.
- 07
Tier and deliver
Tier 1, 2, and 3, segmented and loaded into the CRM your team already uses.
- 08
Refresh
Monthly updates as companies open locations, change leadership, start advertising, or cross a threshold that promotes them a tier.
The output
What a scored account looks like
Five weighted dimensions, one number, one tier. The companies below are invented to show the structure.
A visualization of how D3 scores a market. Companies are invented; the scoring structure is real.
Meridian Freight Systems
Tier 1Logistics SaaS · 620 employees · Chicago, IL
- ICP fit
- 28/30
- Opportunity signals
- 26/30
- Timing
- 17/20
- Potential ACV
- 13/15
- Data quality
- 5/5
- Hiring 6 SDRs
- New VP Sales in March
- Salesforce + Outreach
Cobalt Benefits Cloud
Tier 1HR tech · 240 employees · Austin, TX
- ICP fit
- 25/30
- Opportunity signals
- 22/30
- Timing
- 16/20
- Potential ACV
- 13/15
- Data quality
- 5/5
- Series B in Q2
- Paid ads ramping
- Expanding mid-market team
Harborlight Analytics
Tier 2BI consultancy · 85 employees · Boston, MA
- ICP fit
- 21/30
- Opportunity signals
- 15/30
- Timing
- 11/20
- Potential ACV
- 12/15
- Data quality
- 4/5
- Steady headcount
- No timing trigger yet
Stateline Industrial Supply
Tier 3Distribution · 1,100 employees · Toledo, OH
- ICP fit
- 12/30
- Opportunity signals
- 8/30
- Timing
- 6/20
- Potential ACV
- 9/15
- Data quality
- 3/5
- Weak fit
- Low signal activity
The learning loop
The market map is a living document
Static lists decay. A scored market that ingests conversion data compounds, and every rank has a reason behind it.
D3 Data sits underneath every other function. It decides who outbound reaches, which accounts sales prioritizes, which segments the revenue plan is built on, and eventually which customers look most likely to expand. See how it connects on the full stack.
The loop that makes month six better than month one
- 1
Market data
Accounts, contacts, signals
- 2
Account scoring
Ranked into tiers
- 3
Outbound
2,000+ unique prospects/month
- 4
Responses
Replies classified and routed
- 5
Opportunities
Meetings, pipeline, deals
- 6
Conversion data
What actually closed, and why
Conversion data reweights the scoring model. If accounts with a specific characteristic convert three times better, that signal gains weight, next month's Tier 1 looks different, and rep hours follow what the pipeline proved.
Want to see your market scored?
Bring your ICP to the call. We will walk through what a scoring model for your business would weigh, where the data is strong, and where it gets thin.