VP of Sales

Overview

You're building and running sales at Improvado, a marketing intelligence platform that consolidates data from 1000+ sources for enterprise marketing teams. You'll be hands-on closing deals yourself while simultaneously hiring and coaching AEs, building repeatable processes, and creating the sales playbook. The CRO is being direct: this is the messy scaling phase, not the polished growth stage.

Role Snapshot

Company Context

Stage: Likely Series B/C based on 99 employees and enterprise focus (funding data unavailable)

Size: 99 employees

Growth: Actively building out sales leadership, suggesting expansion phase

Market Position: Challenger in crowded MarTech analytics space - competing against established players and newer AI-focused entrants. Category is hot (AI + marketing data) but also noisy.

GTM Reality

Pipeline Sources:

• 30-40% Inbound - marketing teams researching data consolidation solutions, some from content/SEO
• 50-60% Outbound - you and your AEs prospecting into target accounts (enterprise brands, large SaaS companies)
• 10-20% Referrals/Partners - some from existing customers and implementation partners

SDR/AE Structure: You're likely building this out - probably have 2-4 AEs now, need to decide if you hire SDRs or keep AEs self-sourcing

SE Support: Sales engineers likely needed given technical integration work, but unclear if dedicated or shared pool

Competitive Landscape

Main Competitors: Likely competing against:

• Established BI/analytics tools (Tableau, Looker, PowerBI with connectors)
• Marketing-specific platforms (Datorama, Funnel.io)
• Build-it-yourself data warehouse solutions (modern data stack)
• Point solution vendors ("our current tools are good enough")

How They Differentiate: 1000+ pre-built connectors + AI/agent capabilities for marketers (not just dashboards). Knowledge graph approach vs traditional ETL.

Common Objections:

• "We already have [existing BI tool]"
• "Our data team can build this"
• "Too expensive to consolidate everything"
• "We've tried marketing analytics platforms before and they didn't stick"

Win Themes: Speed to insights, eliminating marketing team's dependency on data/engineering, AI-powered automation that existing tools don't offer

What You'll Actually Do

Time Breakdown

Active Deals (30%) | Team Coaching (25%) | Hiring/Building (20%) | Pipeline Building (15%) | Internal (10%)

Key Activities

• Running Your Own Deals: You're in 3-5 active enterprise deals yourself. Running demos, multi-threading with marketing ops, CMOs, and data teams. Building business cases for 6-figure implementations. Negotiating contracts and navigating procurement.

• Coaching AEs Through Complexity: Your AEs bring you into calls when deals are stuck - IT security is blocking the data connection setup, economic buyer went cold, competing against an internal build. You're figuring out objection handling in real-time because there's no established playbook yet.

• Building the Sales Process: Writing the first version of everything - qualification criteria, demo flow, pricing/packaging positioning, sales stages, forecasting methodology. Testing what works, throwing out what doesn't. Most of it will change in 6 months.

• Hiring and Ramping Reps: Interviewing AE candidates, selling them on joining an early GTM motion. Onboarding new hires when you barely have training materials. Deciding whether to hire SDRs or keep it full-cycle. Writing job descriptions and comp plans.

• Weekly Pipeline Reviews: Running forecast calls where half the deals are in "we're waiting to hear back" status. Figuring out which deals are real and which are wishful thinking. Pushing AEs to multi-thread when they're single-threaded on a champion.

• Product/Marketing Collaboration: Giving product feedback from sales conversations - what features are blockers, what integrations prospects keep asking for. Working with marketing on what content actually helps deals move vs what sounds good in theory.

The Honest Reality

What's Hard

• Building While Running: You're supposed to create repeatable process while nothing is predictable yet. Your pipeline is lumpy, deal stages aren't standardized, and you're learning which industries/personas actually convert. Half your time goes to firefighting instead of building.

• Category Education: Many prospects don't wake up thinking "I need marketing intelligence platform." You're selling against status quo and internal builds. Deals stall because marketing doesn't have budget, or data team says they'll build it, or it gets deprioritized behind other initiatives.

• Hiring in a Tight Market: Finding AEs who can handle ambiguity, technical complexity, AND enterprise sales is hard. The best reps have lots of options. You're competing against more established companies with clearer paths and bigger brands.

• The AI Hype Cycle: Everyone talks about "using AI daily" but most prospects are still figuring out what that means. You're selling future-state value while delivering present-state integrations. Gap between marketing pitch and product reality creates friction.

• Long, Complex Sales Cycles: You're dealing with IT security reviews, data governance policies, integration complexity, and multiple stakeholder alignment. Deals that look close in month 4 can easily slip another quarter. Your forecast accuracy will be rough for the first year.

What Success Looks Like

• Personal Production: You close $1.5-2M yourself in year one while building the team
• Team Scaling: Hire 3-5 strong AEs who can run enterprise deals with coaching
• Process Documentation: Create sales playbook, deal stages, and qualification criteria that new hires can actually use
• Pipeline Predictability: Get to 3x pipeline coverage with improving forecast accuracy (even if it's still imperfect)
• Win Rate Improvement: Figure out ideal customer profile and increase demo-to-close rate from 15% to 25%+

Who You're Selling To

Primary Buyers:

• VP/Director of Marketing Operations (day-to-day user, often your champion)
• CMO (budget holder, cares about marketing ROI and team efficiency)
• VP Data/Analytics (technical evaluator, worried about data governance and integration complexity)
• Sometimes CFO or Rev Ops (in companies focused on marketing efficiency/attribution)

What They Care About:

• Speed to insights: Current process takes weeks to pull cross-channel reports, they need hours/days
• Reducing dependency: Marketing teams tired of waiting on data engineers for every report request
• Data quality and governance: Been burned by bad data before, need confidence in accuracy
• ROI proof: Need to show this pays for itself through better marketing efficiency or revenue attribution
• Integration complexity: Worried about implementation timeline and ongoing maintenance burden
• Team adoption: Past marketing tools failed because reps didn't use them

Requirements

• 8+ years in B2B SaaS sales with at least 3+ years leading/scaling sales teams through the messy middle stage (not just managing mature teams)
• Proven track record building sales from $5M to $20M+ ARR - you've created playbooks, not just inherited them
• Deep experience in MarTech, data/analytics, RevOps, or adjacent categories - you understand the buyer and can talk credibly about marketing data challenges
• Actually using AI tools daily (as the post emphasizes) - not just buzzwords, but understanding how AI changes the pitch and product
• Player-coach mentality - comfortable carrying a quota while building and coaching a team
• Comfort with ambiguity and rapid iteration - this is explicitly described as "messy" scaling, not polished execution
• Enterprise deal experience - complex stakeholder management, 6+ month sales cycles, $100K+ ACV deals
• Track record of hiring and developing AEs who can handle technical, consultative sales