Build the Three Mover Customer Avatars

Objective. Deepen the mover's three drafted personas — relocating family, senior downsizing, commercial office move — into three full nine-cell Segment avatars. Each is anchored on the trigger moment that starts the move, carries the customer's stated pains in their own words, names the hostage-loading and breakage fears beneath them, and fixes the emotional register on the one need every mover buyer shares: Certainty. This is why proof copy converts and discount copy backfires — the whole Foundation inherits that read from here.

Inputs this skill needs

• [Customer Research] — the Foundation's customer slot. For a mover this is not interview transcripts. The trade's honest corpus is: the mover's own Google/Yelp reviews, competitors' 1-star reviews (the gap you position into), quote-request call recordings, and the objections the estimator hears every day. Interviews are a luxury; reviews and objections are the evidence base.
• The three personas already drafted in movers-playbook/company.md § Three Customer Personas — the starting sketch this skill turns into full grids.
• No upstream skills (Wave 1 of the mover Foundation).

ROCKET prompt

ROLE: You are a customer-insights analyst for a moving company. You turn the raw honest corpus a mover already owns — their own reviews, their rivals' 1-star reviews, and the objections their estimator hears — into structured avatars. You infer underlying drives carefully from real language, and you never invent a customer the evidence does not support.

OBJECTIVE: Produce THREE complete Segment avatars — one each for the relocating family, the senior downsizing, and the commercial / office move — plus the shared voice-of-customer list and the trigger-moments list. Each avatar walks all nine cells (Observable Reality → Underlying Drives → Future State), is anchored on its own trigger moment, and lands its dominant need on Certainty. Emit the three Segment objects, the voiceOfCustomer array, the triggerMoments array, and a rank-ordered painIndex — all written through foundation:patch.

CONTEXT: Synthesise SOLELY from the supplied [Customer Research] and the three drafted personas — do not draw on generic knowledge of "people who move" or invent avatar details. The mover avatar has one property no ecommerce avatar has: it is event-triggered and time-bound. Demand is created by an event — a lease ending, a closing date, a job relocation, a downsizing decision, an office-lease event — not by persuasion. You do not create the need; you win the search that follows the trigger. So every avatar starts from its trigger moment, and urgency is structural, not manufactured. Work the three rows in order: Observable Reality (identity, stated pains in their words, stated goals) is the evidence base; Underlying Drives (self-perception, core emotions, underlying fears, dominant need + desired feeling) is inferred from it; Future State (watering holes, cost of inaction, vision of success) is the projection. The emotional register is fixed and must be named: fear is the dominant emotion and Certainty is the dominant core need (Robbins' Six Core Needs — Certainty, Variety, Significance, Love/Connection, Growth, Contribution). Name Certainty explicitly in every avatar's coreNeeds, because it is the load-bearing insight of the whole mover Foundation: a frightened buyer wants proof the number will hold and nothing will break, so proof copy (license lookup, named crew, written not-to-exceed, real reviews) converts and discount copy reads as the scary quote. Use the customer's own language for statedPains, never paraphrase their pain into corporate terms.

The three avatars differ most in who decides and what they fear losing:

• Relocating family — two working adults, kids, a deadline tied to a closing or lease. Trigger: the closing date or lease-end. The mover of the household (often the partner who "handles logistics") decides. Fears a timeline slip that collides with school and work, and the truck-loaded-then-price-jumps horror story.
• Senior downsizing — usually an adult child researching and paying for an aging parent. Trigger: a downsizing decision, a health event, a move to assisted living. Trust-sensitive far more than price-sensitive — picks the safest mover, not the cheapest. Fears strangers handling decades of attachment and a parent being taken advantage of.
• Commercial / office move — a facilities manager or small-business owner, not the end beneficiary. Trigger: an office-lease event. Logistics-driven: minimize downtime, satisfy building-access rules, get a COI before the freight elevator opens. Fears business interruption and a missed COI deadline more than the dollar figure.

KEY INSTRUCTIONS:

1. Build all three avatars as full Segment objects. For EACH avatar populate every field: name, identityContext (open with the trigger moment — "eleven days from a closing date," not "homeowner 25–65"), statedPains (verbatim from the corpus, their words), statedGoals, currentState (include the Schwartz awareness stage — problem-aware / solution-aware / product-aware / most-aware — and its hook), selfPerception, coreEmotions, underlyingFears (name hostage-loading dread and the stranger-handling-grandma's-china / breakage fear explicitly where they apply), coreNeeds (Certainty first, always — with any secondary need after it), desiredFeeling, beliefsAndValues, wateringHoles (specific named channels per persona — GMB, Nextdoor and closing-date search for the family; white-glove reputation and adult-child research for seniors; property-manager and broker referral for commercial — never "social media"), trustedVoices, costOfInaction (for most movers this is literally "book whoever answers the phone first" — which is why speed-to-lead exists), visionOfSuccess ("the truck came, the number held, nothing broke"), and portrait (one 40–70-word human paragraph you could read aloud before writing any copy).
2. Draw statedPains from the ranked-pain corpus as common patterns — DIY exhaustion, last-minute booking, breakage fear, scam / hostage-loading fear, hidden fees, scheduling chaos, price shock on long-distance, damage-liability confusion — expressed in the customer's own voice, and mapped to the avatar most likely to feel each. The ranking is the corpus's ordering of what movers commonly hear; it is NOT a measured frequency for this mover.
3. Ground each avatar in citeable evidence — a review line, an objection the estimator hears, a competitor's 1-star complaint. Label any cell resting on assumption rather than evidence.
4. Capture the shared voiceOfCustomer list: verbatim phrases from the corpus and the mover's own reviews — e.g. "why did my $600 quote become $1,400?", "how do I know you won't hold my stuff hostage?", "will you break my stuff?", "I need this done THIS SATURDAY", "your quote is higher than the other guy". Preserve them as direct quotes; do not smooth them into marketing phrasing.
5. Capture the shared triggerMoments list: lease end · closing date · job relocation · downsizing event · health/assisted-living event · office-lease event. These are the events that create mover demand — the property that distinguishes a mover avatar from any other.
6. Emit a painIndex — the ranked pains as { pain, frequency, intensity }, where frequency and intensity are a qualitative rank order derived from the corpus's ranking and reinforced by the recurrence of the pain in the mover's own reviews. State plainly that these are rank positions, not measured percentages. Never fabricate a statistic, a survey number, or a conversion figure.
7. Run the consistency check the grid demands per avatar: does underlyingFears follow from statedPains? Does visionOfSuccess build on the dominant need (Certainty)? If the cells do not talk to each other, revise the weakest.
8. Do NOT merge the three into a composite. Three precise avatars that each describe one real buyer beat one blended avatar that describes nobody.

EXAMPLES (illustrative shapes, not one mover's data):

• statedPains (their words): "'Why did my $600 quote become $1,400?' — the hidden-fees pain, recurring across the mover's own reviews and the estimator's objection log."
• coreNeeds (need + feeling): "Dominant need: Certainty (secondary: Significance — being treated as more than a load). Desired feeling: that the number is locked, the crew is named, and nothing breaks — proof over persuasion."
• underlyingFears (senior downsizing): "Strangers handling decades of a parent's attachment; a parent being taken advantage of on price. This is the trust-sensitive-not-price-sensitive avatar — the low quote is the scary quote."

TONE & FORMAT: Empathetic yet analytical — objective synthesis of the corpus. American English. The certainty-as-dominant-need read is load-bearing and must be named in every avatar. Customer quotes preserved verbatim. The anonymization law: pains come from the corpus as common patterns, never as measured frequencies; no fabricated statistics — a review count, rate, or percentage appears only if it came from the mover's verified research, never invented. This asset is internal research; customer-facing phrasing within it defers to the mover brand-voice work. Structure exactly as the Output contract.

Output contract

Write to companies/<slug>/playbook/0-foundation/customer-avatar.md:

• # Mover Customer Avatars (three event-triggered segments)
• ## Trigger moments — the shared triggerMoments list: lease end · closing date · job relocation · downsizing/health event · office-lease event, each with the urgency and channel it implies. This is the property that defines a mover avatar.
• ## Avatar 1 — The Relocating Family — the full nine-cell Segment: ### Observable Reality (Identity & Context opening with the trigger moment · Stated Pains in their words · Stated Goals), ### Underlying Drives (Self-Perception & Current State incl. Schwartz stage · Core Emotions & Underlying Fears · Core Needs & Desired Feeling — Certainty named first), ### Future State (Watering Holes & Behaviour · Cost of Inaction · Vision of Success). Close with a > portrait blockquote — one human paragraph.
• ## Avatar 2 — The Senior Downsizing — same nine-cell structure; the trust-sensitive, adult-child-decides avatar.
• ## Avatar 3 — The Commercial / Office Move — same nine-cell structure; the facilities-manager, downtime-and-COI avatar.
• ## Voice of the customer — the verbatim voiceOfCustomer list, each a direct quote from the corpus or the mover's own reviews.
• ## Pain index — the ranked painIndex table (pain · frequency-rank · intensity-rank), both columns stated as corpus rank order, not measured frequency.
• ## Consistency check — one or two lines per avatar confirming fears follow from pains and vision builds on the dominant need, or naming the cell still being revised.

Total length 700–1000 words across the three avatars. Each cell specific enough that reading it triggers a downstream implication. The typed writes: customers.segments (an array of the three Segment objects), customers.voiceOfCustomer, customers.triggerMoments, and customers.painIndex — all through foundation:patch. Parseable by mover-foundation-blueprint-assemble, which reads all three avatars into the Customer Context block.