Customer Discovery

Customer discovery is structured learning, not selling

RelayOps is a B2B (business-to-business, selling to companies) SaaS (software as a service, subscription software delivered over the internet) venture improving dispatch and scheduling for mid-market field-service companies and the anchor venture for ENT 301. Founders Maya Chen (CEO, former dispatch manager at regional HVAC operator Summit Climate) and Jordan Okonkwo (CTO, former platform engineer) left Summit Climate in 2025 after living dispatch-center chaos firsthand. Their initial beachhead is 80-to-200 technician residential-heavy HVAC and plumbing firms, later expanding to commercial HVAC in Phoenix and Dallas with 50 to 150 field technicians. Discovery work confirmed 10 to 15 percent overtime on peak weeks and missed first-visit appointment windows tied to same-day capacity loss when dispatchers rebalance schedules across phone calls, whiteboards, and legacy CRM tabs without a live view of technician skill, location, and parts. Competitors include ServiceTitan (heavy and expensive for mid-market), spreadsheets and whiteboards (status quo).

Throughout this course, RelayOps evolves from opportunity hypothesis to scaled venture. Elective depth lives in ENT 401 when you want a full unit on that phase. ENT 301 teaches the integrated journey so you can advise founders, invest, or launch with disciplined evidence. Unit 1 applies the full customer validation toolkit ENT 401 teaches in one elective depth track.

Customer discovery is the systematic process of turning opportunity hypotheses into interview evidence, field observations, and synthesized patterns before you scale product investment. RelayOps enters Unit 1 with H1 promoted: same-day dispatch rebalance pain in 80-200 technician HVAC and plumbing firms. The goal is not demos. The goal is falsifiable evidence on who hurts, who pays, and what workflow must change.

Discovery differs from validation in degree of structure. Early discovery is exploratory. Later discovery in this unit becomes hypothesis-driven with role coverage, instruments, and stop rules. ENT 401 Unit 3 covers interview design at textbook depth; ENT 301 integrates the operating rhythm Maya and Jordan run weekly.

RelayOps allocates 12 interview hours per week with Friday synthesis. At $45,000 monthly burn, unfocused discovery is a luxury. Every hour must tie to a ranked assumption.

The evidence ladder

RelayOps labels evidence levels to prevent grade inflation. Level 1: paid behavior (pilot deposit, signed SOW). Level 2: strong intent (written LOI with metrics). Level 3: patterned interviews (same story across roles). Level 4: single rich interview or shadow. Level 5: anecdote or friend praise.

Customer discovery targets level 3+ before MVP build. Founders love level 5 because it feels good. Investors should ask for level labels on every claim.

The ladder keeps RelayOps honest when dispatchers enthuse but COOs stay silent.

Evidence ladder applied to RelayOps claims:

Buying units in B2B discovery

B2B purchases involve users (dispatchers), champions (ops directors), economic buyers (COO/owner), and blockers (IT/security). RelayOps maps each account: dispatcher ground truth, ops director pain rank, COO budget hook, IT parallel track.

Discovery fails when only champions who attend conferences are interviewed. Champions love strategy; dispatchers reveal Tuesday.

Role coverage per account beats high meeting count across random titles.

RelayOps buying unit map per target account:

Discovery operating cadence

RelayOps weekly cadence: Monday recruit/screen, Tuesday-Thursday interviews/shadows, Friday synthesis and assumption updates. Screening uses segment screener: 80-200 tech, residential-heavy mix, Phoenix/Dallas metro, dispatcher on staff.

Screener rejects look like good logos but wrong learning fit (enterprise module users, sub-40 tech shops). Evidence pollution costs more than polite decline.

Synthesis produces coded notes: pain codes, spend mentions, tool artifacts, role disagreements. Codes feed opportunity score updates.

Discovery versus sales calls

Sales calls propose solutions and negotiate price. Discovery calls explore past behavior and current workarounds. RelayOps discovery scripts open with purpose: "We are studying same-day rebalance workflows; we are not selling today."

Mixing modes produces false positives: prospects agree to features to be nice. Keep discovery blind to pricing until WTP instruments in later lessons.

Founders must resist premature demos. Jordan's prototype appears only after problem language stabilizes.

Ethics, access, and reciprocity

Discovery access requires consent, anonymized notes, and benchmark summaries as reciprocity. RelayOps shares aggregated overtime bands, not other firms' names.

Trust in tight vertical communities compounds. Broken confidentiality closes ten doors at association dinners.

Minimal disruption: schedule shadows after morning triage when possible.

H1 promoted. RelayOps plans 14 days: 10 interviews, 3 shadows, 2 IT calls. Assumptions: A1 dispatchers describe >10 min loops, A2 COO overtime hook, A3 IT review <60 days.

Part A: Instrument assignment

Part B: Capacity check

12 interview hours/week × 2 weeks = 24 hours. Avg 1.5 hours per session including notes → 16 sessions max. Plan 15 sessions with 1 buffer.

Check: 15 ≤ 16 capacity ✓

Part C: Stop rule

If A3 shows >60 day security review in 4/5 IT calls, escalate integration partner strategy before Gate 2 commit.

Part D: Managerial read

Investors should see assumption-linked instruments, not "we are doing customer discovery." Operators should see role coverage per account.

FieldForce (fictional) founders attended five conferences in 90 days, collected 200 business cards, and claimed "500 discovery conversations." Zero shadows, zero overtime PDFs, zero IT calls. Evidence stayed level 5. RelayOps's cadence produces fewer meetings and higher decision grade.

RelayOps has 18 dispatcher meetings and 2 COO meetings after 3 weeks. Dispatchers rate pain 4.5/5. COOs rate 2.8/5.

Tasks: (1) Identify discovery failure mode. (2) Rebalance next two weeks' calendar. (3) State evidence level for "COO funds software."

Solution

(1) Role imbalance; buyer gap risk.

(2) Cap dispatchers at 6 more; schedule 6 COO and 4 IT calls; pause new dispatcher unless paired with COO same account.

(3) Level 4 anecdote for funding; not level 2 intent.

Check: COO n=2 insufficient for pattern ✓

Key takeaways

• Customer discovery produces labeled evidence, not meeting counts.
• B2B discovery maps users, champions, buyers, and blockers per account.
• Weekly cadence with screeners and synthesis prevents pollution.
• Keep discovery separate from sales and premature demos.
• Ethics and reciprocity sustain access in tight vertical communities.

1. Label three RelayOps claims with evidence levels.
2. Draft a screener question that rejects sub-80-tech shops.
3. Continue to Lesson 2: Problem Interviews.

Applying Customer Discovery at RelayOps

When RelayOps applies customer discovery, Maya Chen and Jordan Okonkwo anchor decisions in field evidence, not slide optimism. Their beachhead (80-to-200 technician residential-heavy HVAC and plumbing firms, later expanding to commercial HVAC in Phoenix and Dallas with 50 to 150 field technicians) experiences 10 to 15 percent overtime on peak weeks and missed first-visit appointment windows. Discovery interviews suggested $89 to $149 per technician per month in discovery interviews. Competitors include ServiceTitan (heavy and expensive for mid-market), spreadsheets and whiteboards (status quo). Every framework in this lesson should translate into a falsifiable claim about that segment, not generic startup advice.

Consider how customer validation and interview evidence changes capital allocation. RelayOps started with roughly $400k runway and ~$45k monthly burn before seed. A one-month delay on the wrong opportunity costs more than a month of disciplined interviews. That is why customer discovery is a CEO-level skill, not a brainstorming exercise.

Document owners alongside metrics. Maya owns discovery synthesis; Jordan owns build scope tied to assumption ranks; both sign kill criteria before pilots. When definitions live in a shared glossary (pilot versus beta, activation versus login), the team avoids comparing incompatible cohort charts after Dallas expansion.

Extended RelayOps scenario: cross-functional read

Imagine RelayOps's quarterly review for customer discovery. An angel investor asks whether dispatch pain justifies another build sprint. A pilot COO asks whether overtime reduction pays for software. A dispatcher lead asks whether the console survives Monday heat-wave call volume. A weak customer validation and interview evidence answer pleases one stakeholder. A strong answer links evidence: interview prevalence, timed shadow data, pilot median dispatch time, and renewal intent.

Work a conservative arithmetic example. Suppose RelayOps targets 100-technician firms at $28 per technician per month ($2,800 MRR per logo). Closing 18 beachhead logos yields $50,400 MRR ($605k ARR). If CAC (customer acquisition cost, sales and marketing to win one paying customer) is $18,000 per logo, payback in months equals CAC divided by monthly gross profit. At 80% gross margin on MRR, monthly profit ~$2,240; payback ~8 months. Check: 18,000 / 2,240 ≈ 8.0 ✓. Founders who skip this math raise before they know whether GTM is repeatable.

Stakeholder conflict is normal. Jordan may push feature breadth; Maya must protect RAT (riskiest assumption test, cheapest experiment that falsifies the highest-impact uncertain belief) scope. Customer Discovery gives language to negotiate with pre-registered metrics rather than charisma. If evidence is descriptive only, label it and fund the next test instead of scaling spend.

For deeper study on this unit's specialty, see ENT 401. ENT 301 integrates the full arc; electives provide textbook-depth units you can take after this core course.

Technical mechanics and checks (RelayOps patterns)

For customer discovery, show work the way finance shows reconciliations. Opportunity scorecards print weighted criteria and explicit kill rules. Interview synthesis tables show code frequency with qualified denominators only. MVP scorecards list assumption rank, build weeks, runway share, and kill criteria. Cap tables after SAFE conversion show pre-money, post-money, and founder ownership with check lines.

Use plain-language hypotheses before instruments. Example: "If fewer than six of ten operations leaders rank same-day rebalance in top-three pains, RelayOps deprioritizes hypothesis H1." That hypothesis is falsifiable without code. Weak hypotheses hide inside feature roadmaps.

Spreadsheet grain matters. Customer-level tables suit funnel conversion; logo-month tables suit retention; assumption-level tables suit experiment backlogs. RelayOps forbids ambiguous metrics like "engagement" without operational definitions tied to dispatch jobs routed per active day.

Common executive questions (and disciplined answers)

Executives ask short questions that require long disciplined answers. "How sure are we?" maps to evidence labels (exploratory, descriptive, causal), not bravado. "What is the dollar impact?" maps to overtime saved, slots recovered, or MRR with stated assumptions. "Can we ship faster?" maps to risk of untested adoption during live emergencies. "Why not copy ServiceTitan?" maps to wedge focus and beachhead economics, not feature envy.

RelayOps's credible answer format for customer discovery is three bullets: recommendation, evidence strength, and next test if limitations matter. A fourth bullet states what would falsify the recommendation within 60 days. That discipline prevents founders from becoming either bottlenecks or rubber stamps for investor narratives.

Judgment under uncertainty (RelayOps decision log)

Founders who master customer discovery keep a decision log: date, decision, evidence at time, dissent captured, review date. When RelayOps chose emergency-queue MVP over full suite parity, the log recorded HeatRoute's LOI-to-active failure mode as contrast case. When Phoenix beat Dallas on retention, the log triggered segment screener review rather than blaming sales tone.

Your workbook should mirror that log format for one venture you follow. If you cannot write dissent and kill criteria, you have a story, not a decision. Customer Discovery is how teams convert stories into capital-efficient learning.

Applying Customer Discovery at RelayOps

When RelayOps applies customer discovery, Maya Chen and Jordan Okonkwo anchor decisions in field evidence, not slide optimism. Their beachhead (80-to-200 technician residential-heavy HVAC and plumbing firms, later expanding to commercial HVAC in Phoenix and Dallas with 50 to 150 field technicians) experiences 10 to 15 percent overtime on peak weeks and missed first-visit appointment windows. Discovery interviews suggested $89 to $149 per technician per month in discovery interviews. Competitors include ServiceTitan (heavy and expensive for mid-market), spreadsheets and whiteboards (status quo). Every framework in this lesson should translate into a falsifiable claim about that segment, not generic startup advice.

Consider how customer validation and interview evidence changes capital allocation. RelayOps started with roughly $400k runway and ~$45k monthly burn before seed. A one-month delay on the wrong opportunity costs more than a month of disciplined interviews. That is why customer discovery is a CEO-level skill, not a brainstorming exercise.

Document owners alongside metrics. Maya owns discovery synthesis; Jordan owns build scope tied to assumption ranks; both sign kill criteria before pilots. When definitions live in a shared glossary (pilot versus beta, activation versus login), the team avoids comparing incompatible cohort charts after Dallas expansion.

Extended RelayOps scenario: cross-functional read

Imagine RelayOps's quarterly review for customer discovery. An angel investor asks whether dispatch pain justifies another build sprint. A pilot COO asks whether overtime reduction pays for software. A dispatcher lead asks whether the console survives Monday heat-wave call volume. A weak customer validation and interview evidence answer pleases one stakeholder. A strong answer links evidence: interview prevalence, timed shadow data, pilot median dispatch time, and renewal intent.

Work a conservative arithmetic example. Suppose RelayOps targets 100-technician firms at $28 per technician per month ($2,800 MRR per logo). Closing 18 beachhead logos yields $50,400 MRR ($605k ARR). If CAC (customer acquisition cost, sales and marketing to win one paying customer) is $18,000 per logo, payback in months equals CAC divided by monthly gross profit. At 80% gross margin on MRR, monthly profit ~$2,240; payback ~8 months. Check: 18,000 / 2,240 ≈ 8.0 ✓. Founders who skip this math raise before they know whether GTM is repeatable.

Stakeholder conflict is normal. Jordan may push feature breadth; Maya must protect RAT (riskiest assumption test, cheapest experiment that falsifies the highest-impact uncertain belief) scope. Customer Discovery gives language to negotiate with pre-registered metrics rather than charisma. If evidence is descriptive only, label it and fund the next test instead of scaling spend.

For deeper study on this unit's specialty, see ENT 401. ENT 301 integrates the full arc; electives provide textbook-depth units you can take after this core course.