Upmarket SDR

Overview

You prospect into finance teams (CFOs, Controllers, AR Managers) at companies with $500K-$2M+/month in accounts receivable. Your job is booking qualified demos for AEs. You spend most of your day cold calling, researching target accounts, and running multi-touch sequences. You're selling AI voice technology for invoice collection—something buyers need but are cautious about because it touches sensitive client relationships.

Role Snapshot

Company Context

Stage: Seed/Early Series A (YC S23, ~2 years old)

Size: 21 employees

Growth: Early GTM stage—you're building the upmarket motion from scratch. Zoya (Senior Sales Manager) is hiring, which signals they're scaling from founder-led sales.

Market Position: Category challenger. Competing against traditional collection agencies, legal firms, and manual AR processes. They're positioning as the "relationship-preserving" AI alternative.

GTM Reality

Pipeline Sources:

• 80%+ Outbound - You're building lists, cold calling, cold emailing, LinkedIn messaging
• 10-15% Inbound - Some YC network referrals, website inquiries from companies researching solutions
• 5-10% Warm intros - Existing customer referrals, investor network

SDR/AE Structure: Small SDR team (you're one of the first "upmarket" SDRs) feeding AEs. You book, they close.

SE Support: Likely no dedicated SEs yet at this stage—AEs handle technical demos themselves.

Competitive Landscape

Main Competitors:

• Traditional collection agencies (older, relationship-damaging reputation)
• Legal demand letter services
• Manual AR processes (internal teams calling/emailing)
• Other fintech AR automation tools

How They Differentiate: AI voice calls with sentiment analysis, "relationship-preserving" approach vs aggressive agencies, 50% recovery rate vs 5-6% industry average, enterprise-grade (zero incidents track record).

Common Objections:

• "We handle collections internally"
• "Our clients will be upset if a robot calls them"
• "What if your AI damages our client relationships?"
• "We already use [collection agency/legal firm]"
• "Our AR team has it under control"

Win Themes: Proof of 50% recovery rates, speed (20 days vs 4-6 months), white-label option (looks like it's coming from them), track record with similar enterprise clients.

What You'll Actually Do

Time Breakdown

Prospecting (50%) | Outreach (30%) | Follow-up/Admin (20%)

Key Activities

• List Building & Research: 1-2 hours/day identifying companies with high AR volumes. You're looking at financial reports, LinkedIn company pages, news about growth/expansion (signals of higher invoicing). You build lists by industry, company size, funding rounds.

• Cold Calling: 50-70 dials/day to CFOs, VPs of Finance, AR Managers. Most calls go to voicemail. Gatekeepers are common. You're trying to get 2-3 conversations per day where you can pitch a 15-min discovery call.

• Email/LinkedIn Sequences: Multi-touch cadences (call, email, LinkedIn, call, email over 2 weeks). You personalize based on company AR challenges—mentioning DSO trends, industry collection issues, recent growth news.

• Discovery/Qualification: When you get someone on the phone, you ask about their AR volume, current collection process, DSO metrics, pain points with slow-paying clients. You're qualifying if they have $500K+ monthly AR and an actual pain point.

• Internal Handoffs: Slack/CRM notes for AEs on qualified meetings. You attend some first calls to learn what converts, iterate your messaging.

The Honest Reality

What's Hard

• Getting past gatekeepers to CFOs/Controllers - Executive assistants block most calls. Finance leaders are busy and don't always take cold calls. You'll get a lot of "send me an email" brush-offs.

• The "we handle it internally" objection - Most companies think their AR team is fine, or they're uncomfortable with AI touching client relationships. You're fighting status quo bias and skepticism of automation.

• Long research cycles - Finding companies with actual AR pain takes time. You can't just blast generic lists—you need to identify companies with invoice volume issues, which requires digging.

• Educating a new category - This isn't Salesforce or Slack. People don't know "AI invoice collection" is a thing. You spend time explaining what it is, why it's safe, how it's different from debt collectors.

• Early-stage chaos - Messaging will change. ICPs will shift. You're figuring out the playbook as you go. Expect pivots.

What Success Looks Like

• Booking 15-20 qualified meetings/month with finance leaders at target accounts ($500K+ monthly AR)
• 30-40% of your meetings convert to demos (AEs take over from there)
• Building repeatable sequences that others can use as the team scales

Who You're Selling To

Primary Buyers:

• CFOs at mid-market companies ($50M-500M revenue)
• VPs of Finance / Controllers at larger enterprises
• AR Managers / Credit Managers (influencers, not final decision-makers)

What They Care About:

• Cash flow improvement - Unpaid invoices tie up working capital
• Client relationship risk - They're terrified of damaging customer relationships with aggressive collection tactics
• DSO reduction - Days Sales Outstanding is a key metric they're measured on
• Team bandwidth - Their AR teams are underwater chasing down payments manually
• Proof it works - They want case studies from similar industries/company sizes before trusting AI to call their clients

Requirements

• 2-3+ years SDR/BDR experience, ideally some exposure to enterprise/upmarket motions
• Comfortable cold calling senior finance leaders (CFOs, Controllers)—not just mid-level managers
• Experience in fintech, SaaS selling to finance, or B2B services is helpful (you need to speak their language—DSO, AR aging, cash conversion cycles)
• High tolerance for rejection and iteration—this is early-stage, you're building the playbook
• Self-starter mentality—21-person company means less structure, more figuring it out yourself
• Willing to dig into research (finding high-AR-volume companies isn't as easy as pulling a ZoomInfo list)