Sales Representative

Overview

You're selling Brox's digital twin technology—AI replicas of real people that predict human decisions. Your buyers are typically insights teams, product leaders, or innovation executives at mid-to-large companies who currently use traditional research methods (surveys, focus groups, A/B tests). You'll spend most of your time explaining what's even possible with this tech, then proving it works.

Role Snapshot

Company Context

Stage: Early-stage (likely Seed/Series A based on 19-person size)

Size: 19 employees total

Growth: Actively hiring multiple sales roles, suggests recent funding or revenue traction

Market Position: Category creator—digital twins for behavioral prediction is novel. You're not competing against other twin platforms, you're competing against traditional market research methods and "gut feel" decision-making.

GTM Reality

Pipeline Sources:

• 20-30% Inbound - Some traffic from people searching for alternatives to traditional research, but limited since category is new
• 60-70% Outbound - Cold outreach to research teams, product leaders, innovation groups at companies spending on surveys/research
• 10% Referrals - Early customers telling peers, but still building this motion

SDR/AE Structure: Likely self-sourcing early on (they're hiring for "sales roles" plural, may be building SDR function)

SE Support: Unknown, but demos are highly technical—you'll need to deeply understand how the twins work and show live predictions

Competitive Landscape

Main Competitors: Not direct competitors, but competing budgets/mindshare:

• Traditional market research firms (Ipsos, Kantar, etc.)
• Survey platforms (Qualtrics, SurveyMonkey)
• User research tools (UserTesting, Wynter)
• Internal "we'll just run our own A/B test" mentality

How They Differentiate: Speed and scale—unlimited experiments vs. paying per survey wave. Predictive accuracy vs. stated intent. Can test things before building them.

Common Objections:

• "How do we know the digital twins actually predict real behavior?" (need validation case studies)
• "This sounds like science fiction" (need to prove it's real, validated tech)
• "We already have a research vendor" (budget reallocation conversation)
• "Our data science team could probably build this" (positioning against build vs. buy)

Win Themes: ROI from failed launches, time-to-insight compression, testing things that are impossible/expensive to test in real life

What You'll Actually Do

Time Breakdown

Prospecting/Outreach (35%) | Demos & Discovery (30%) | Deal Advancement (25%) | Internal (10%)

Key Activities

• Prospecting research: Identifying companies with research budgets, finding the right buyers (not always obvious—could be CMO, CPO, insights director, innovation lead). Building lists, personalizing outreach.
• Category education calls: Most first calls are "wait, what?" conversations. You're explaining digital twins, showing examples, getting them to understand the concept before they can even evaluate if they need it.
• Customized demos: Showing how twins work with their specific use cases—can we predict their customer's behavior? Can we test their product concepts? Each demo needs customization to their industry/problem.
• Proof of concept management: Many deals require a pilot/POC. You're coordinating the twin build, managing expectations, tracking accuracy, and turning successful tests into contracts.
• Multi-stakeholder navigation: Research buyers need to bring in IT (data privacy), finance (budget), legal (contracts), sometimes procurement. You're herding cats across departments.

The Honest Reality

What's Hard

• You're teaching a new category: Most prospects have never heard of behavioral digital twins. You spend tons of time just getting people to understand what you do. Many calls end with "interesting, but I don't know if we need this."
• Proof is everything: Prospects are skeptical (rightfully). You need case studies, accuracy data, validation stories. Early on, you won't have many. Expect lots of "show me it works for someone like us."
• Long, winding cycles: Even interested buyers take months to figure out budget, get internal buy-in, run a pilot, then decide. Deals slip quarters regularly.
• Small team = limited support: At 19 people, you don't have a huge content library, polished enablement, or a big CS team to handle POCs. You're wearing multiple hats.
• Explaining AI without overhyping: You need to make AI/digital twins sound real and credible, not like vaporware. Finding that line is tricky.

What Success Looks Like

• Closed 6-8 deals per year at $50K-150K each
• Built a repeatable pitch that gets prospects from "huh?" to "I need to test this" in 2-3 calls
• Strong case studies from early customers that prove twin accuracy
• Shortened POC-to-contract timeline as you learn what proves value fastest

Who You're Selling To

Primary Buyers:

• Head of Insights / VP Market Research (budget owners for research spend)
• Chief Product Officer / VP Product (want to test ideas before building)
• Innovation / Strategy leaders (looking for edge in decision-making)
• Occasionally CMO (testing campaigns, messaging, positioning)

What They Care About:

• Accuracy: Will these twins actually predict what real people do? Need proof.
• Speed: Can they test 10 ideas in a week vs. running a month-long survey for one question?
• Cost per insight: Is this cheaper than their current research spend? What's the ROI?
• Data privacy/ethics: How are twins built? Whose data? What are the ethical implications?
• Integration: How does this fit into their existing research workflow?

Requirements

• Comfortable selling something that requires education—you won't be clicking through a standard 10-slide deck
• Technical enough to understand AI/ML concepts and explain them in plain English
• Patience for long sales cycles with multiple stakeholders and proof requirements
• Self-starter who can build outbound pipeline without a big SDR team
• Able to work East Coast hours (company requirement)
• Smart and "moderately interesting" (per founder's own words—they want people who can hold a conversation)