Note: This is the take-home assignment submitted before joining Orum. The MVP recommendation here — embedded sales collateral — became the foundation for the AI-powered features built after joining. The post-call AI notes that shipped are covered in the Orum AI Notes case study.

Context

Orum connects sales teams to their target prospects using a parallel dialer with speech recognition — dialing multiple numbers at once and automatically routing reps to live conversations. The platform powers over 2 million dials per month across SDR teams at B2B companies.

The core insight driving the assignment: three factors determine whether an SDR books a meeting — how prepared they are before the dial, how well they manage the conversation once connected, and how effectively leadership identifies and coaches around weaknesses. The task was to propose a product vision and solution that moves the needle on all three.

Constraints: 3 engineers, 1 designer, 4–6 months.

Vision

Enabling sales teams to accelerate conversions by driving effective and meaningful conversations with prospects, while providing an industry-leading sales enablement platform.

The vision isn't about making more dials — Orum already does that. It's about making the dials that connect actually land. Preparation before, resources during, coaching after.

Personas

Four personas across the SDR org, with the entry-level SDR as the primary focus.

Amanda Jones
Entry-Level SDR — Primary Focus

23, SF/Remote. First job in sales. Tech-comfortable, eager to learn. Struggles to drive conversations and pivot quickly. Loses focus under pressure.

Dan Smith
Mid-Level SDR

36, Fairfield CT. High achiever, startup background. Wants better prep tools. Hates context shifting. Values efficiency above everything.

Mark Hernandez
Sales Leader

54, NYC. Wants a high-performing team. Reluctant to adopt new tools. Needs data to justify changes.

Martha Cook
SDR Manager

42, Boston. Middle manager focused on team development. Wants self-service tools for her reps. Frustrated by slow enterprise tech adoption.

Why entry-level SDR? Strategic feature development could allow a novice SDR to perform at a senior level — without the cost of hiring senior talent. That's a cost savings for the business while achieving its primary objective: more booked meetings. Entry-level SDRs are also more open to new tools and faster to adopt, which makes the feedback loop tighter and rollout cleaner.

Research

Before proposing anything, I went to talk to the users — not hypothetically, but actually.

15 SDR interviews via LinkedIn Reddit SDR communities G2 reviews TrustRadius reviews

Four themes surfaced consistently:

  • Pre-call research: SDRs want prospect information at a glance — without opening five tabs before a dial
  • Embedded sales collateral: Battle cards, objection handling, and talking points accessible during the call, not in a Google Doc no one opens
  • Integrated calendar: Booking a meeting mid-call requires switching apps — every extra step risks losing the moment
  • Coaching tools: SDRs want to improve, and managers want visibility into who needs what

The G2 word cloud analysis reinforced this: "time," "efficient," "calls," "conversations," "booked," and "collected" dominated — users measuring themselves in volume and outcomes, which means anything that improves their conversion rate per connected call has direct, measurable impact.

Enhancements

The proposed features map directly to the three moments that determine outcomes: before, during, and after the call.

Before

Pre-Call Prep

  • Embedded LinkedIn profile — prospect info without context switching (context shifting reduces productivity 20–40%)
  • AI conversation analysis — if the prospect has been contacted before, AI surfaces key notes, keywords, and sentiment from prior calls
During

In-Call Assist

  • Integrated sales collateral — battle cards, objection handling, talking points accessible in one click without leaving the dialer
  • Integrated calendar — book the meeting without switching tabs
  • AI in-call recommendations — real-time prompts based on live conversation analysis, personalized by industry and persona
After

Post-Call Coaching

  • AI coaching tools — analyze conversation patterns across the team, surface common objections, highlight what top performers do differently
  • Segment-level analysis — cohort and rep-level breakdowns for managers

Prioritization

With 3 engineers and a half-time designer for 4–6 months, not everything ships in the first wave. The impact/effort matrix drives the sequencing.

Quick Wins — ship first
High impact · Low effort
Embedded LinkedIn Embedded Collateral AI Post-Call Notes
Major Projects — phase carefully
High impact · High effort
AI Conversation Analysis AI Coaching Tools AI In-Call Recommendations
Fill-Ins — add between phases
Low impact · Low effort
Calendar Integration
Backlog — defer
Complex AI with compliance questions
In-call AI suggestions

MVP Recommendation

Embedded sales collateral: objection handling and battle cards. The research made this clear — SDRs aren't losing calls because they don't know the product. They're losing calls because they can't find the right response fast enough when a prospect pushes back. The collateral exists; it's just not accessible when it matters.

The calendar integration is useful but not critical. When a prospect agrees to a meeting, they've already said yes — a few extra seconds to switch apps won't kill the conversion. Prioritizing collateral first means the first release directly improves conversion rate, which is the metric Orum and its customers care most about.

Backlog Rationale

The complex AI features — in-call suggestions and coaching tools — require significant engineering effort and have Legal/Compliance implications around recording and analyzing call data. Worth building, but not before validating the simpler features and understanding the data governance landscape first.

Roadmap

Q1
Embedded LinkedIn Embedded SDR Collateral (MVP) Beta → Internal Testing
Q2
Embedded SDR Fixes AI Conversation Analysis (design + build) Beta → Bug fixes
Q3
AI Bug Fixes AI Beta GA Release
Future
AI Coaching Tools In-Call AI Recommendations Calendar Integration

Success Metrics

Within the first three months post-launch, these are the KPIs that matter — all of them outcome-focused, not feature-adoption-focused.

Meetings scheduled — the clearest signal that in-call performance improved. If collateral is working, conversion rate per connected call goes up.
Average call time per prospect — a proxy for how quickly SDRs can find what they need and deliver it. Faster access to collateral means shorter disposition time and more dials per session.
Sales accepted leads — measures whether the meetings being booked are quality meetings. Better-prepared SDRs should be booking better-fit prospects, not just more meetings.