Discovery Call Plan & Qualification Notes — Tech Sales (SDR/BDR → AE) Capstone — SkilsMVP

What you'll produce

A discovery call plan and a set of filled-in call notes for your top account — the artifact that decides whether a deal is real before you ever build a demo. It has two halves: (1) a plan of 8–10 open-ended, non-leading questions sequenced to uncover the prospect's process, pain, impact, and decision path, and (2) notes from the call that qualify the deal against BANT (Budget, Authority, Need, Timing), quantify the pain in hours and dollars, and end in an honest qualify-in or qualify-out decision with the next step. This is the single most important habit that separates reps who hit quota from reps who burn a quarter demoing to people who were never going to buy. It proves the Topic 6 skills — selling by listening, qualifying hard, and connecting a real need to a real outcome — and it's the bridge between your outreach (Deliverable 2) and your tailored demo (Deliverable 4): you can only tailor a demo to pain you actually discovered and wrote down.

Instructions

Set the call goal and pre-call hypothesis. Before the call, write one sentence on the outcome you want (almost always: "earn a tailored demo with the right people in the room") and 2–3 things you already suspect are true from your "why now" research, e.g. their order volume doubled, they're hiring agents, Trustpilot complaints about response time. You're going in to confirm or kill these, not to assume them.
Write 8–10 open-ended, non-leading questions and sequence them: current state ("Walk me through how a support ticket gets handled today") → problem ("What's the most frustrating part of that for your team?") → impact / quantify ("How many tickets a day, and how long does a first reply take?") → why now ("What changed that made you take this call?") → decision process ("If this works, who else weighs in, and how does a tool like this get bought here?"). Every question must be answerable in a sentence or paragraph, not "yes/no," and must not put words in their mouth (ask "what happens when a ticket sits too long?" not "isn't slow response hurting you?").
Plan to talk less than you listen. Mark 2–3 places where your only job is to shut up and take notes. Add one or two follow-up "dig deeper" prompts ("Say more about that" / "What does that cost you?") so a vague answer becomes a quantified one.
Run the call (or role-play it) and take real notes. Capture the prospect's actual words where you can — quotes are gold for the demo and for forecasting. Write what they said, not what you wish they'd said.
Quantify the pain. Turn their answers into numbers: tickets/day, minutes per ticket, hours/week lost, agents affected, and a dollar figure (hours × loaded hourly cost, or revenue/churn at risk). A pain you can't put a number on is a pain you can't sell against — and a number you make up is worse than none. Show your math and label assumptions.
Fill in BANT honestly. For each of Budget, Authority, Need, and Timing, write what you learned, mark it confirmed / partial / unknown, and note the evidence. "Unknown" is an honest answer that tells you your next question; a checkbox you ticked because you hope it's true is how deals die in stage 3.
Map the buying committee. Name your champion (who feels the pain and will sell internally), the economic buyer (who controls budget), and the users. Note who was on the call and who still needs to be.
Make the call: qualify-in or qualify-out. State the decision in one line, the reason, and the single committed next step with an owner and a date (a booked demo with the buyer in the room beats a vague "send me info"). If you qualify out, say why and what would have to change to bring it back — qualifying out fast is a win, not a failure.

Worked example

(Account: GearGrid — 220-person DTC outdoor-gear retailer. Product: Lumadesk, an AI-powered support helpdesk at ~$40/agent/month. Call: 28-minute Zoom discovery with Maya Chen, Head of Customer Support, booked off the Deliverable 2 sequence.)

Call goal: Earn a tailored demo with Maya and her VP of Ops (who owns the budget). Pre-call hypothesis to confirm or kill: (a) order volume roughly doubled this year, (b) the support team is underwater and hiring, (c) slow first-response time is showing up in public Trustpilot reviews.

Discovery question plan (asked in this order):

"Before I show you anything — walk me through what happens from the moment a customer emails support to the moment it's resolved today." (current state)
"Where in that flow do tickets get stuck or fall through the cracks?" (problem)
"On a normal day, how many tickets does the team handle, and how big is the team right now?" (quantify)
"What's your current first-response time, and where do you wish it was?" (quantify / gap)
"What changed recently that put this on your plate now?" (why now)
"When a customer waits too long for a reply, what does that actually cost you — refunds, chargebacks, bad reviews, churn?" (impact / dollarize)
"What have you already tried to fix it, and why hasn't that been enough?" (rules out 'do nothing' and competitors)
"If we wave a magic wand and this is solved six months from now, what's different for your team and your customers?" (desired outcome)
"Who else feels this pain or would need to weigh in before a tool like this gets adopted?" (authority / committee)
"When teams here buy software like this, how does that usually work — budget, approval, timeline?" (budget + timing + process)

Call notes (what Maya actually said):

Current state: Whole team lives in a shared Gmail inbox plus a spreadsheet to track "who's got what." No real ticketing. "By Monday morning it's chaos — we genuinely don't know what's been answered."
Volume / team: ~600 tickets/day, up from ~300 a year ago after order volume doubled. 8 agents today, hiring 3 more (the trigger from my research — confirmed). Peak is Mon–Tue and post-promo.
First response: "Honestly? Right now it's about 14 hours. Pre-growth we were under 4. We tell ourselves the goal is 4."
Where it breaks: Duplicate replies (two agents answer the same email), VIP/wholesale tickets buried under noise, and agents rewriting the same answers all day (shipping delays, returns, sizing).
Why now: "Our Trustpilot dropped to 3.1 and three reviews this month literally said 'no one ever replied.' My VP saw it. That's why you got this meeting." (Public-review pain — confirmed.)
Impact: Maya estimates ~20% of tickets are repetitive questions agents retype from scratch. Returns/refund requests that age out past the policy window have caused goodwill refunds she ballparks at ~$6–8k/month. Worried about churn but doesn't have a hard number.
Tried already: Canned Gmail templates ("nobody keeps them updated") and a hiring plan — "but throwing bodies at a broken process is expensive and slow."
Desired outcome: First response under 4 hours, no duplicate replies, agents stop retyping the same answers, Trustpilot back above 4.0.
Committee: Maya = champion (owns the metric, feels it daily). Devon Apraku, VP of Operations = economic buyer, controls the tooling budget, "saw the reviews." The 8 agents = users. SE/IT not needed for a tool this size.
Process/budget/timing: "Devon can approve a few thousand a month without going higher. We're hiring now, so if this lets us handle growth without adding even more headcount, the timing is perfect — ideally live before our Q4 holiday rush."

Quantified pain (with math, assumptions labeled):

Slow response → revenue at risk: Goodwill refunds from aged-out tickets ≈ $6,000–$8,000/month (Maya's estimate) → ~$72k–$96k/year.
Wasted agent time on repetitive replies: 600 tickets/day × 20% repetitive = 120 repetitive tickets/day. Assume ~5 min each to hand-write = 10 agent-hours/day ≈ 50 hours/week. At a loaded cost of ~$25/hr (assumption: ~$18/hr wage × 1.4 loaded) = ~$1,250/week ≈ $65k/year of agent time spent retyping answers an AI draft could pre-fill.
Headcount avoidance: They're adding 3 agents (~$45k loaded each = ~$135k/year) partly to keep up with volume; if AI drafting + ticketing cuts handle time, some of that hiring pressure eases.
Lumadesk cost for comparison: 11 agents × $40 × 12 = ~$5,280/year. Even crediting only the refund leakage, the pain is ~14–18x the cost — a clean ROI story for the demo.

BANT qualification:

Budget — Partial. Maya says Devon can approve ~$2–5k/month without escalation, and our ~$440/month list sits comfortably inside that. Evidence: the champion's account of Devon's authority — not Devon's own confirmation. Gap: the spend is relayed second-hand; Devon was never on the call, so the budget is plausible but not verified. Confirming it is the same action that closes the Authority gap — get Devon in the room and have him own the number.
Authority — Partial. Maya is the champion but not the buyer; Devon (VP Ops) is the economic buyer and was not on this call. Next step must put Devon in the room.
Need — Confirmed. Real, quantified, public pain (14h response, 3.1 Trustpilot, 50 hrs/wk wasted, $72k+/yr leakage). No-pain-no-sale risk = low.
Timing — Confirmed. Active trigger (order volume doubled, hiring now, VP watching reviews) and a deadline (live before Q4 holiday rush).

Decision — QUALIFY IN. Strong, quantified, urgent need with a clear champion, a price that the relayed budget dwarfs, and a real deadline. The two open gaps — Authority and Budget — are really one gap wearing two hats: both are "Partial" only because the economic buyer hasn't been engaged, and both close the moment Devon is in the room and owns the number himself. Committed next step: Maya to bring Devon to a 45-minute tailored demo, booked for next Thursday, focused only on the three things she named — ticketing/no-duplicates, AI reply drafting for repetitive tickets, and a response-time dashboard. (If Maya can't get Devon in the room within two weeks, that's a yellow flag on the deal's real authority — re-test before investing demo prep.)

Rubric

The app's AI scores the learner's submission against these criteria and gives feedback. Levels: Needs work (1) / Solid (2) / Excellent (3). Passing = every criterion at Solid or above.

Open-ended, non-leading questions — 1: yes/no or leading questions that pitch instead of probe · 2: 8–10 genuinely open questions · 3: open questions logically sequenced (current state → problem → impact → why-now → decision) that would make a real buyer open up.
Listening over pitching — 1: notes are mostly the rep talking/selling · 2: notes capture the prospect's situation in their words · 3: notes capture verbatim quotes and dig deeper on vague answers to surface the real pain.
Pain quantified in hours/dollars — 1: pain is vague ("they're busy") · 2: at least one concrete number · 3: a credible, shown-your-work cost (tickets/day, hours/week, $/year) with assumptions labeled.
BANT qualified honestly — 1: a wishful checklist or missing parts · 2: all four covered with evidence · 3: all four with confirmed/partial/unknown status, evidence, and the gaps named (not glossed over).
Buying committee mapped — 1: no roles identified · 2: champion/buyer/user named · 3: roles named with who was on the call vs. who must be engaged next.
Clear qualify decision + next step — 1: no decision or a vague "send info" · 2: a stated qualify-in/out with a next step · 3: a justified decision and a single committed next step with owner and date (or an honest qualify-out with what would change it).
Coherence with prior deliverables — 1: disconnected from the account · 2: targets the same account/ICP from Deliverables 1–2 · 3: clearly continues the deal story and sets up the tailored demo (Deliverable 4) on the pain discovered here.