Squarely Puzzles — STRATEGY
Problem
Squarely is an indie jigsaw-puzzle line invented by the founder’s dad — three KDP-published paperback titles plus an iOS app in development — generating ~$850 lifetime royalty across 368 units, but distribution is lumpy and network-driven (90%+ of units are Volume 1; recent months are 1-2 units). The bet needs to convert a network-funded launch into a self-sustaining engine before the catalog and product attention atrophy.
Approach
Treat Amazon Search/Browse as the load-bearing content engine and fuel + lubricate it before adding new acquisition surfaces. Per the Racecar Framework diagnosis in growth-strategy.md, Squarely is in Pitfall #2 — mistaking turbo boosts (personal pushes, social posts) for an engine. The strategy is: (1) lubricate the existing engine via KDP listing + A+ copy + review-generation discipline, (2) add fuel by shipping more puzzle SKUs (Volumes 2-3 already exist; iOS app and two unpublished games are pipeline fuel), (3) layer a paid loop (Amazon Ads) on top once we can measure it, and (4) explore a casual-contact viral loop for the iOS app (Wordle-style shareable). The why-this-not-that: don’t build the website-as-engine or build content-marketing first; the cheapest unit of growth is fixing the channel that already converts.
Personas
- Network buyer (today’s actual buyer): friend/family of the founder or his dad. Reached via personal push. Not a sustainable persona; documented as the current state, not the target.
- Amazon puzzle searcher: shopper browsing Amazon for puzzle books / unique puzzle gifts. Reached via KDP listing + Amazon Ads. The target persona for Engine #1.
- iOS casual gamer (future): mobile player who enjoys Wordle-class daily-puzzle formats. Reached via App Store + casual-contact viral share. Pre-launch.
- TBD — needs founder distillation: the specific buyer-segment hypothesis (gift-giver vs self-buyer? puzzle hobbyist vs casual? US-only vs international?). The growth-strategy.md doesn’t pick one and it materially changes ad targeting + listing copy.
Key metrics
- Lifetime paperback units: 368 (as of 2026-04-27 KDP snapshot)
- Lifetime royalty USD: $850.97
- Monthly units (recent): 1-2/month (Mar-Apr 2026); peak was 113 in Aug 2023 launch month
- Critical-component metric (founder-picked 2026-04-30): Amazon ads ACoS / ROAS / campaign-level conversion — not yet instrumented
- iOS app: in development; no launch metrics yet
- Per-bet P&L: revenue – KDP fees – ads spend – fulfillment — partially instrumented; cost-routing discipline gap per yaml
Work tracks
- Amazon-ads instrumentation + actuator (CRITICAL — sensors+tools layer): build visibility into ACoS/ROAS/campaign conversion AND the ability to programmatically modify campaigns. Owner: Ray. Status: gap (founder pick 2026-04-30; Notion task
352f7d49-36d1-816e-abed-e2c2304e08a2). - KDP listing + A+ copy optimization: tighten description, keywords, A+ assets on the three existing books. Owner: Ray draft → founder review. Status: planned (queued in Apr 23 next-moves audit).
- iOS app launch + casual-contact viral loop: ship the app, design the shareable result format. Owner: founder + dad. Status: in development; no launch date.
- Catalog fuel — publish Volumes 4+ and unpublished games: production pipeline expansion. Owner: founder + dad. Status: backlog.
- Cost-routing discipline in Monarch: tag every Squarely-related expense so the bet-level P&L isn’t polluted by other bets. Owner: Ray. Status: partial.
- Website-to-Amazon inbound loop (side-bet, not-yet-inventoried): can squarely.app drive measurable inbound to KDP? Owner: TBD. Notion task
352f7d49-36d1-816c-a1c4-c5e767343562.
Targeting system (RDCO 4-layer thesis)
Sub-process: not yet named for Squarely (per yaml sub_process: []). Most plausible candidate: puzzle-product viability (design quality + production cost + pricing power), per the bet-architecture playbook table. TBD — needs founder distillation: is the sub-process targeting “puzzle quality + price-per-puzzle margin,” or something else?
P&L meta-layer (canonical): bootstrapped P&L. revenue – kdp_fees – ads_spend – fulfillment_cost > 0 monthly. Per the recursive-structure thesis, sub-process gains that violate this layer get vetoed.
Instrumentation (RDCO 4-layer thesis)
Built: monthly KDP report exports (manual; parsed via kdp/parse-kdp-orders.py); basic website analytics; Monarch MCP cost visibility (partial).
Gaps: Amazon ads performance tracking (founder-picked critical component — currently blind to ACoS/ROAS/conversion); cost-routing discipline (per-bet expense tagging in Monarch); website-tracking depth not documented.
Tools (RDCO 4-layer thesis)
Built: website-update push (standard build/deploy); Amazon A+ copy review/create (manual today, productizable); mailing-list (Resend); image generation (xAI — quality “so-so but workable”).
Gaps: run/modify Amazon ads programmatically (coupled with the ads sensor — sensor without actuator is wasted info); Meta Ads instrumentation + run/modify (fast-follow per founder, Notion task 352f7d49-36d1-81c4-b2c5-e81a8b1d548b); organic-traffic posting (no social/blog/community-posting capability today).
Feedback loop (RDCO 4-layer thesis)
Partial — formalize. Have signals (KDP exports, Monarch costs, website tracking) and have actuators (website push, A+ copy, mailing list), but no structured loop that says “P&L outcome → diagnostic on which layer’s bottleneck shifted → next experiment to run.” Decision-traces will be auto-logged via /log-bet-decision against 07-bet-stacks/squarely.yaml. Cadence + owner for the diagnostic-to-experiment cycle is TBD — needs founder distillation.
Open questions for founder
- Which buyer segment is the targeting hypothesis (gift-giver vs self-buyer vs hobbyist vs casual)? The current strategy doc doesn’t commit and it changes ad targeting + listing copy.
- Is there a named sub-process targeting system beyond P&L (puzzle quality? margin per SKU? something else)?
- What’s the cadence + owner for the formalized feedback loop (weekly review? monthly? Ray runs it autonomously and surfaces findings, or founder runs it with Ray’s prep)?
- iOS app launch date and whether the casual-contact viral loop is in v1 scope or v2.
- KDP listing + A+ copy optimization is queued — go/no-go on Ray drafting v1 against the three existing books?
Related
- ../../06-reference/2026-04-30-rdco-thesis-targeting-systems-feedback-loops
- ../../06-reference/2026-04-30-rdco-bet-architecture-playbook
- ../../07-bet-stacks/squarely
- growth-strategy
- kdp/2026-04-27-summary