Most reps target accounts by title alone. "VP of Finance." "Director of IT." Titles tell you who might own budget. They tell you nothing about who is exercising it right now.
This play goes deeper. It combines org hierarchy, live buying intent, hiring patterns, and recent technology investment into one composite score, so every contact in your target accounts is ranked by proximity to budget authority, with a data-backed reason to reach out.
1. Build your target audience
Start at the company level. Pull target accounts from your CRM, a B2B data platform, or a CSV, then filter to your ICP: industry, employee band, geography. Optionally exclude existing customers or require a competitor install.
Keep the list focused. Around 7,000 accounts or fewer works best if you plan to layer AI enrichment later, since AI research runs per record.
Then surface the people. Generate a contact list across Finance, Procurement, IT, and Operations at C-Suite, VP, and Director level. Start broad. The scoring in step 3 does the filtering for you.
2. Layer on the buying signals
Enrich every contact with the data points the score is built on:
Management level and department. C-Suite and VP contacts in Finance, Procurement, or IT sit structurally closest to budget.
Budget and procurement intent. Live research into topics like budget planning and enterprise software purchasing is a timing signal, not a static title.
Finance and procurement hiring. Open roles across FP&A, budgeting, and sourcing often precede new vendor evaluations.
Recent technology investment. A tool added in the last 6-12 months proves budget existed and was spent. One of the strongest "they have money and they spend it" signals available.
Promotions and new hires into budget roles. New budget holders are most receptive to vendor conversations early in their tenure.
Verified company news (pain points, projects, hiring plans) adds qualitative color that structured data misses.
3. Score proximity to budget
Combine the signals into a single Budget Proximity Score out of 13:
Management level: C-Suite +4, VP +3, Director +2, Manager +1
Finance, Procurement, or IT department: +2
Budget intent: High +3, Medium +2, Low +1
Active finance or procurement hiring: +1
Recent technology investment: +2
Recent promotion or new hire into a budget role: +1
Then tier every contact: 9 or more is an Economic Buyer, 5-8 a Budget Influencer, below 5 a Technical or User Contact. One note: intent data is always High, Medium, or Low, never a number, so score it conditionally rather than multiplying it.
4. Turn the data into talking points
With the score in place, use an AI layer to make it usable at speed:
Budget authority rationale. A 1-2 sentence explanation per contact of why they are likely close to budget, synthesized from the signals above.
First-touch email. A short draft referencing that rationale, so outreach shows its research. Build this last, since it depends on the rationale.
Example outreachSubject: Quick question about [Company]'s [relevant initiative]Hi [Name], I noticed [Company] has been investing in [specific signal]. That caught my attention because companies in your space going through similar changes are often evaluating new vendor solutions around this time. I'd love to share how we help teams like yours navigate this. Would you have 15 minutes this week for a quick call?
5. Activate
Push the output to where reps work:
Create views per tier so Economic Buyers and Budget Influencers are one click away.
Route by territory and export to your CRM, with the rationale and draft email attached to each contact.
Refresh weekly or monthly. Intent, hiring, and technology signals shift constantly, and contacts should move between tiers as they do.
How to run this play in GTM Studio
FAQs for budget holder targeting
Why isn’t targeting by job title enough?
Titles show who might own budget; they say nothing about who is exercising it right now. Live intent, hiring activity, and recent technology spend separate an active economic buyer from a name on an org chart.
What data does this play need?
A target account list plus third-party B2B data covering org structure, buying intent, job postings, and technographics. No customer or first-party data is required.
How should intent data be handled in the score?
Intent is always High, Medium, or Low, never a number. Score it conditionally (High +3, Medium +2, Low +1) rather than trying to multiply it.
How many accounts should I run this on?
Keep to around 7,000 or fewer if you are layering AI enrichment, since AI research runs per record and costs scale with list size. You can always run further batches.
How often should the play refresh?
Weekly or monthly. Budget cycles, hiring, and intent move constantly, and contacts should shift between tiers as the signals do.