{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5ca1-7489-b635-bddd0638a82c", "slug": "dd4f58a21c5fc1e5", "title": "e-Conomy SEA 2023 report: Philippines", "subtitle": "Google", "author": "Google", "pageCount": 14, "kind": "consulting-deck", "orientation": "landscape", "aspectRatio": 1.777, "document": { "id": "019dd923-5ca1-7489-b635-bddd0638a82c", "slug": "dd4f58a21c5fc1e5", "title": "e-Conomy SEA 2023 report: Philippines", "rawTitle": "e-Conomy SEA 2023 report: Philippines", "authorId": "Bain", "authorName": "Google", "authorDisplay": "Google", "kind": { "slug": "consulting-deck", "label": "Consulting deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "e-Conomy-SEA-2023-report-Philippines.pdf", "sourceUrl": "https://services.google.com/fh/files/misc/philippines_e_conomy_sea_2023_report.pdf", "sourceDate": null, "presentationDate": null, "ingestedAt": "2026-04-23 20:51:31+00", "status": "ready", "pageCount": 14, "orientation": "landscape", "aspectRatio": 1.777, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/dd4f58a21c5fc1e5", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 14, "pagesIndexed": 14, "pagesWithImages": 14, "pagesWithMetadata": 14, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 84, "chartCount": 29, "metricSlides": 6, "toolMatches": 65, "documentToolMatches": 0, "frameworkMatches": 0, "arcMatches": 1, "beatMatches": 5, "loopMatches": 4, "patternMatches": 0, "storymakersMatch": true, "scoreAvailable": true, "reviewAvailable": true, "activistThesisAvailable": false, "pitchdeckMetadataAvailable": false, "pitchdeckProfileAvailable": false }, "blocks": [], "matches": { "arcs": [ { "arc": { "name": "The Onion", "slug": "onion", "status": "active", "bestFor": "Root cause analysis, diagnostics", "canonId": "019dd956-627f-76b1-b286-0c575846d572", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Surface Observation -> First Layer -> Deeper Layer -> Core Insight -> Implications", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "matchId": "019dd95a-0543-7581-99b1-2b6fee4c5e03", "evidence": "Layered analytical reveal: country macro overview (p2) → market sizing (p3) → geographic & industry detail (p4-5) → core HVU segment insight (p6) → investor implications (p7); Tagalog half (8-14) repeats the same arc.", "isPrimary": true, "confidence": 60, "extraction": { "at": "2026-04-29 13:07:28.977035+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "beats": [ { "to": 2, "arc": { "name": "The Onion", "slug": "onion", "status": "active", "bestFor": "Root cause analysis, diagnostics", "canonId": "019dd956-627f-76b1-b286-0c575846d572", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Surface Observation -> First Layer -> Deeper Layer -> Core Insight -> Implications", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "from": 1, "name": "Surface Observation", "beatId": "019dd95a-07a6-74c7-9026-c4388d6b3c88", "matchId": "019dd95a-0543-7581-99b1-2b6fee4c5e03", "beatType": { "name": "Surface Observation", "slug": "onion-surface-observation", "status": "active", "canonId": "019dd9b8-0b39-73c0-b415-6c4cf34cc343", "version": 1, "description": null }, "evidence": "Cover divider then 'Country overview' macro summary panels", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 60 }, { "to": 3, "arc": { "name": "The Onion", "slug": "onion", "status": "active", "bestFor": "Root cause analysis, diagnostics", "canonId": "019dd956-627f-76b1-b286-0c575846d572", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Surface Observation -> First Layer -> Deeper Layer -> Core Insight -> Implications", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "from": 3, "name": "First Layer", "beatId": "019dd95a-07a6-74c7-9026-c84bab471e39", "matchId": "019dd95a-0543-7581-99b1-2b6fee4c5e03", "beatType": { "name": "First Layer", "slug": "onion-first-layer", "status": "active", "canonId": "019dd9b8-0bd1-75b2-9773-18ea7796621c", "version": 1, "description": null }, "evidence": "Market sizing: ~$35B by 2025 across digital economy verticals", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Development", "slug": "development", "status": "active", "canonId": "019dd9b8-0332-7008-b3aa-11b0840a57b1", "version": 1, "description": "The middle stretch in arcs that have one. Often where length problems creep in." }, "alignedBlockIds": null, "matchConfidence": 60 }, { "to": 5, "arc": { "name": "The Onion", "slug": "onion", "status": "active", "bestFor": "Root cause analysis, diagnostics", "canonId": "019dd956-627f-76b1-b286-0c575846d572", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Surface Observation -> First Layer -> Deeper Layer -> Core Insight -> Implications", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "from": 4, "name": "Deeper Layer", "beatId": "019dd95a-07a6-74c7-9026-cd7ab46a5553", "matchId": "019dd95a-0543-7581-99b1-2b6fee4c5e03", "beatType": { "name": "Deeper Layer", "slug": "onion-deeper-layer", "status": "active", "canonId": "019dd9b8-0c69-7473-8222-610daa3e4df7", "version": 1, "description": null }, "evidence": "Geographic gap maps and DFS sub-segment growth charts", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Development", "slug": "development", "status": "active", "canonId": "019dd9b8-0332-7008-b3aa-11b0840a57b1", "version": 1, "description": "The middle stretch in arcs that have one. 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Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 60 }, { "to": 7, "arc": { "name": "The Onion", "slug": "onion", "status": "active", "bestFor": "Root cause analysis, diagnostics", "canonId": "019dd956-627f-76b1-b286-0c575846d572", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Surface Observation -> First Layer -> Deeper Layer -> Core Insight -> Implications", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "from": 7, "name": "Implications", "beatId": "019dd95a-07a6-74c7-9026-d5561784f3b1", "matchId": "019dd95a-0543-7581-99b1-2b6fee4c5e03", "beatType": { "name": "Implications", "slug": "onion-implications", "status": "active", "canonId": "019dd9b8-0dda-7730-893d-deaac7e9c295", "version": 1, "description": null }, "evidence": "Funding collapse from pandemic highs signals investor caution", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Reflection", "slug": "reflection", "status": "active", "canonId": "019dd9b8-0510-73fd-8d00-1c7e45f7bc98", "version": 1, "description": "The \"so what\" beat. 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The deck is fully duplicated in Tagalog (pages 8-14 mirror 1-7), so methodology fidelity is reflected once and repeated.", "imagesInspected": 6, "extractionSeconds": 276.28845 }, "score": { "backend": "codex", "scoredAt": "2026-05-02 15:11:48.339+00", "subScores": { "scqa_arc": 52, "action_titles": 82, "mece_structure": 62, "closing_strength": 25, "evidence_quality": 82, "clarity_of_thesis": 68, "production_quality": 76, "visual_storytelling": 78 }, "totalScore": 66, "coveragePct": 100, "explanations": { "scqa_arc": "The deck has Situation and some Complication signals in slides 2-7, but it reads more like a country data pack than a clear Situation-Complication-Question-Answer narrative.", "action_titles": "Most analytical slides use insight-bearing titles, for example slide 3: \"Philippines is expected to continue its double-digit climb towards ~$35B by 2025, largely fueled by e-commerce.\"", "mece_structure": "The sequence covers macro context, market size, geography, DFS, user segments, and funding, but the pillars are not framed as a mutually exclusive answer set and slides 8-14 repeat the same structure in Tagalog.", "closing_strength": "The deck ends on slide 14 with a funding chart rather than a recommendation, decision, implications page, or next-step call to action.", "evidence_quality": "Most substantive claims are backed by charted GMV, DFS, geography, HVU, and funding data on slides 3-7 and 10-14, with weaker support on the overview slides 2 and 9.", "clarity_of_thesis": "The central argument is partly visible by slides 3-5, where growth, Metro Manila concentration, and DFS upside emerge, but slide 2 is only a generic overview and no single thesis is stated upfront.", "production_quality": "The Bain-style layouts are consistent and footnoted, with strong title discipline on data slides, but duplicated translated content and overcrowded slides 6 and 13 hold back polish.", "visual_storytelling": "Charts, maps, and segment comparisons generally match the message on slides 3-7 and 10-14, though slides 6 and 13 are overcrowded and reduce visual focus." }, "slidesAnalyzed": 14 }, "review": { "backend": null, "verdict": "A competent country-profile excerpt from a regional atlas with good action-title discipline on the data slides, but it is not a Storymakers exemplar — use slides 3, 4 and 6 as teaching cases for quantified action titles, and use the whole chapter as a counter-example of an analytical tour that never commits to an SCQA arc or recommendation.", "reviewedAt": "2026-04-24 16:49:29+00", "slidesSeen": 14, "suggestions": [ "Replace p.2 'Country overview' with a one-page SCQA: the situation (SEA digital economy growing), the complication (Philippines DFS funding just collapsed), the question ('where is the next $35B actually going to come from?'), and a one-line answer that the rest of the chapter then proves", "Add a closing 'so-what' slide after p.7 that turns the five data points into 2-3 implications for operators or investors — e.g. 'Back HVU-facing transport plays, not generalist DFS'", "Rewrite topic-label titles into insight titles: p.5 should name the driver ('DFS growth is a low-base story, not a penetration story'), and the section dividers should preview the argument rather than repeat the country name" ], "closingScore": 22, "openingScore": 38, "topStrengths": [ "Quantified, declarative action titles on analytical slides (p.3 '$35B by 2025', p.6 '6.3X vs non-HVUs', p.4 'Metro Manila leads; gap widens beyond capital')", "Disciplined one-metric-per-slide structure — each page carries a single crisp number ($35B, 6.3X, $0.2B) which is excellent pull-quote hygiene", "Full-deck Tagalog translation (p.8-14) is a rare accessibility move that signals serious local-audience intent" ], "topWeaknesses": [ "No SCQA or recommendation anywhere — the chapter is pure atlas, with p.2 'Country overview' as a topic label rather than a question or complication", "Closes on funding contraction (p.7 / p.14) with no interpretation or call to action — readers are left with a negative data point and no guidance", "Half of the page count is translation (p.8-14 mirrors p.1-7 verbatim) — measured as a standalone narrative, the deck has only 7 unique slides of content", "Section divider titles ('Country spotlight: Philippines', p.1 / p.8) are labels, not MECE pillars — no argumentative skeleton is visible" ], "narrativeScore": 42, "pillarCritique": "There is only one real section divider ('Country spotlight: Philippines' / its Tagalog twin) and it opens the chapter rather than organising MECE pillars — the five data slides are a topic sequence (size -> geography -> industry -> segments -> funding), not named buckets of an argument.", "closingCritique": "The English half closes on p.7 with 'Funding dropped substantially from pandemic highs' — a downer data point with no 'so what,' no recommendation, and no next steps. 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