attribution · 스킬 지원 에이전트
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X, 검색, 이메일에서 유입이 생기는데 어느 쪽이 실제 가입이나 구매로 이어졌는지 궁금할 때 사용합니다. 마지막으로 클릭한 채널만 볼지 처음 알게 된 채널도 볼지 기준을 정리합니다.
각 도구의 기간, 전환 정의와 집계 방식이 같아야 비교가 가능합니다. 숫자만 넣기보다 수집 방법과 누락 가능성도 알려주세요. 측정되지 않은 경로를 확정적인 매출 기여로 단정하지 않고 현재 자료로 설명할 수 있는 범위를 나눕니다.
attribution으로 X 링크, 검색, 메일 유입의 가입 전환을 비교해줘. 아래 세 보고서의 집계 기준 차이를 먼저 확인하고 확실한 결론과 추가로 필요한 데이터를 나눠줘.
받을 수 있는 결과기여도 해석과 지표 차이 설명
스킬 지원 에이전트와 채널별 분석 자료가 필요합니다. 광고·분석 계정의 자동 접근 권한은 별도입니다.
현재 사용 중인 스킬 지원 에이전트에 아래 요청을 붙여넣으세요. SKILL.md만 복사하면 보조 파일이 빠질 수 있어요.
https://github.com/coreyhaines31/marketingskills 에서 skills/attribution 스킬을 현재 에이전트의 스킬 폴더에 설치해줘. SKILL.md와 이 스킬이 참조하는 scripts·references·assets와 공통 파일을 함께 유지하고, 필요한 실행 환경과 추가 연결을 확인한 뒤 알려줘. 설치가 끝나면 attribution 스킬을 인식하는지 확인해줘.
“attribution 스킬을 사용해줘. 입력 자료: 채널별 방문·전환·집계 기준”처럼 요청하세요. 위의 예시를 내 자료에 맞게 바꿔도 좋아요.
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제작자 원본 2026-10-04 확인 · 설치 안내 2026-10-04 확인 · 실제 설치·실행 미검증
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설치 안내 출처--- name: attribution description: When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo. metadata: version: 1.1.2 --- # Attribution You help users answer the hardest question in marketing: **which of my efforts actually caused this conversion and this revenue?** Attribution is where marketers lose the most money — to channels that look good in one dashboard and terrible in another, to "direct" and "branded search" that hide the real source, and to models that quietly encode an opinion as if it were fact. This skill has two pillars. Know which one the user needs before you dive in: - **(A) Interpretation** — choosing an attribution model, picking a measurement approach, and *reconciling the conflicting numbers* your tools report. This applies to everyone, even with zero engineering. - **(B) Own your attribution (first-party)** — instrumenting and stitching attribution *yourself* when you control the site/app. This is the build track. Use it when the user says "I want to track this myself" or is hitting a conversion that lives on a domain they don't own. Most requests start with (A). Reach for (B) only when they control the surface and want to build. Product context: check for `.agents/product-marketing.md` and read it if present — business type, sales cycle, and primary conversion drive almost every recommendation here. ## Boundaries — what this skill does NOT own State these up front so you don't rebuild neighboring skills: - **General event tracking, tracking plans, UTM setup, GA4/GTM** → **analytics**. Attribution *assumes tracking exists*. The line: analytics = "what events and how to fire them"; attribution = "how touches join to conversions and survive to revenue."--- name: attribution description: When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo. metadata: version: 1.1.2 --- # Attribution You help users answer the hardest question in marketing: **which of my efforts actually caused this conversion and this revenue?** Attribution is where marketers lose the most money — to channels that look good in one dashboard and terrible in another, to "direct" and "branded search" that hide the real source, and to models that quietly encode an opinion as if it were fact. This skill has two pillars. Know which one the user needs before you dive in: - **(A) Interpretation** — choosing an attribution model, picking a measurement approach, and *reconciling the conflicting numbers* your tools report. This applies to everyone, even with zero engineering. - **(B) Own your attribution (first-party)** — instrumenting and stitching attribution *yourself* when you control the site/app. This is the build track. Use it when the user says "I want to track this myself" or is hitting a conversion that lives on a domain they don't own. Most requests start with (A). Reach for (B) only when they control the surface and want to build. Product context: check for `.agents/product-marketing.md` and read it if present — business type, sales cycle, and primary conversion drive almost every recommendation here. ## Boundaries — what this skill does NOT own State these up front so you don't rebuild neighboring skills: - **General event tracking, tracking plans, UTM setup, GA4/GTM** → **analytics**. Attribution *assumes tracking exists*. The line: analytics = "what events and how to fire them"; attribution = "how touches join to conversions and survive to revenue." - **Ad-platform pixels, CAPI, server-side conversion tracking** → **ads** (`references/conversion-tracking.md`). Attribution consumes platform-reported numbers and corrects for their bias; it doesn't set up the pixels. - **Pipeline stages, lead lifecycle, CRM revenue dashboards** → **revops**. Attribution feeds pipeline data; it doesn't define stages. - **Showing up in / measuring AI search** → **ai-seo**. Attribution names AI traffic as a blind spot only. --- ## Pillar A — Interpretation ### 1. What attribution can and can't tell you Set expectations before touching a number: - **Attribution is directional, not truth.** It's a model of causality built from incomplete data (cookies expire, sessions fragment, offline touches vanish, people research on one device and buy on another). Treat it as a strong hint, never a verdict. - **Every model is an opinion.** "First-touch" says the first ad gets all the credit; "last-touch" says the closing click does. Both are wrong in opposite directions. Choosing a model is choosing whose story to believe — say so out loud. - **The attribution gap is normal.** The sum of channel-reported conversions almost always exceeds real conversions, because every platform claims credit for the same sale. Your job is to shrink and explain the gap, not to make the numbers tie out perfectly. They won't. When a user demands one true number, reframe: "We can get you a *defensible, consistent* number and a read on which channels are trending up. A single objective truth doesn't exist — here's why, and here's what we use to make decisions anyway." ### 2. Attribution models The classic rule-based set plus data-driven — and when each one lies. **Last non-direct** is a last-touch variant (skip the junk drawer), not a separate school of thought. | Model | Credit rule | Best for | How it lies | |---|---|---|---| | **First-touch** | 100% to the first known touch | Top-of-funnel / demand-gen valuation; short cycles | Ignores everything that closed the deal; over-credits awareness channels | | **Last-touch** | 100% to the last touch before conversion | Direct-response, quick e-comm | Over-credits bottom-funnel + branded search/direct; ignores what created demand | | **Last non-direct** | 100% to last touch, skipping "direct" | A cheap fix for direct pollution | Still single-touch; just moves the blind spot | | **Linear** | Equal credit to every touch | Long, multi-touch journeys where every step matters | Treats a throwaway visit like a demo; flatters high-frequency channels | | **Time-decay** | More credit to touches nearer conversion | Longer cycles where recency matters | Under-credits the top of funnel; still an assumption, not a measurement | | **Position-based (U-shaped)** | 40% first, 40% last, 20% middle | B2B with clear "created" + "closed" moments | The 40/40/20 split is arbitrary; middle touches get shortchanged | | **Data-driven (algorithmic/Shapley)** | Credit from modeled marginal contribution | High-volume accounts with enough conversions | A black box; needs volume; can't see offline/dark touches it was never fed | **Platform availability note:** Google Ads and GA4 retired first-click, linear, time-decay, and position-based as selectable reporting models (2023). Those UIs offer **data-driven** and **last-click** (plus GA4's paid-channels last-click variant). Teach the full table as *concepts* and as models you can compute on your own event path / CRM / warehouse — not as Google dropdown options. **Rules of thumb:** - Never report a single model in isolation for a long sales cycle. Show **first-touch and last-touch side by side** — the truth lives between them, and the gap between them *is* the insight. - Data-driven attribution needs volume. Google Ads historically gated DDA behind ~3,000 ad interactions and ~300 conversions in 30 days; hard minimums are gone and DDA is the default, but Google still recommends ~**200 conversions and ~2,000 ad interactions** in 30 days for quality. Below that, DDA often collapses toward last-click priors — noise dressed as science. On Google/GA4 when thin: prefer last-click (or accept that DDA ≈ last-click). Elsewhere (first-party / CRM / warehouse): compute position-based or first+last side by side. - The model matters far less than being **consistent** and pairing it with an out-of-model sanity check (Pillar A §4, self-reported). For the model math, worked examples of one journey scored six rule-based ways, and Shapley explained plainly, see `references/attribution-models.md`. ### 3. The three measurement paradigms Models split credit *within* your tracked data. Paradigms are how you get at *causality* — increasingly rigorous, increasingly expensive: | Paradigm | What it is | Answers | Needs | Watch out | |---|---|---|---|---| | **MTA** (multi-touch attribution) | Stitch user-level touches, apply a model | "Which touchpoints appear on converting journeys?" | Clean cross-device user-level tracking | Cookie loss + privacy gut user-level data; click-paths systematically over-weight search/direct and under-weight impression channels | | **MMM** (media/marketing mix modeling) | Top-down regression of spend vs. outcomes over time | "What's each channel's aggregate contribution, including offline/brand?" | 2–3 yrs of weekly data, spend variation | Correlational; slow to react; needs real budget swings to learn | | **Incrementality** (geo holdout, PSA, ghost ads, on/off) | Controlled experiment: exposed vs. withheld | "Did this channel *cause* lift I wouldn't have gotten anyway?" | Ability to withhold; enough volume for significance | The gold standard, but you can only test a few things at a time | **How to choose:** small budget / short cycle → good UTM + last-non-direct + a self-reported survey beats a fancy model. Mid budget, several channels → MTA for day-to-day + periodic incrementality tests on your biggest line items. Large budget, offline + brand spend → MMM for the portfolio + incrementality to validate MMM's coefficients. Incrementality is the tiebreaker whenever two channels both claim the same conversions. Decision table by budget × sales cycle × channel count, and how to *read* a geo-holdout / PSA test (not a stats tutorial), in `references/measurement-paradigms.md`. ### 4. Self-reported attribution The most underused signal, and often the most honest for long cycles and dark social. A post-conversion "How did you hear about us?" survey catches what tracking structurally cannot: podcasts, word of mouth, Slack communities, a founder's tweet, "a friend told me." - **When it beats tracking:** long consideration cycles, high word-of-mouth, brand/community-led, or heavy dark-social (see §5). If a big slice of your journeys are "direct," you have a self-reported-shaped hole. - **Ask at the moment of conversion** (signup, first purchase, demo request) — highest recall, before memory fades. - **Wording:** open-ended ("How did you first hear about us?") captures dark social; a short pick-list is easier to quantify but pre-biases the answer. Best practice: pick-list of your known channels **plus a free-text "other/tell us more."** - **Treat it as a triangulation input, not gospel** — recall is fuzzy and people credit the *memorable* touch, not the first. **Discount survey share when turning it into credit** (e.g. 22% recall of YouTube is evidence of awareness, not a claim that YouTube deserves 22% of conversions). Pair with incrementality / platform deltas before writing an allocation rule. - On the build side, this is a form field written to your CRM/analytics as a person property — see Pillar B and `references/first-party-tracking.md`. ### 5. Reconciling conflicting sources The request behind most attribution work: **"Google says 50, Meta says 40, GA says 60, my CRM says 35 — who's right?"** Nobody is. Here's the framework. **Why each source systematically lies:** | Source | Biased toward | Because | |---|---|---| | **Ad platforms** (Google/Meta/LinkedIn) | Over-counts *itself* | Claims view-through + click conversions in its own window; every platform counts the same sale; motivated to look good | | **GA / web analytics** | Last non-direct click | Loses cross-device, loses cookie-blocked users, dumps the unknown into direct | | **CRM** | Whatever the rep typed / the form captured | Human entry, lead-source overwrites, offline deals with no digital trail | | **Self-reported survey** | The *memorable* touch | Recall bias; under-counts boring-but-real touches like retargeting | **How to triangulate:** 1. **Pick one source of truth for the conversion count** — usually your CRM or backend (the system where money is real). Everything else explains *where those came from*, they don't get to redefine *how many*. 2. **Never sum across platforms.** If Google and Meta both claim a conversion, you have one conversion with two claimants, not two conversions. De-dupe against the source-of-truth total. 3. **Read directional agreement, not absolute match.** If every source says paid search is up and organic is down this quarter, that trend is trustworthy even though no two numbers match. 4. **Use self-reported as the tiebreaker** when platforms fight over the same conversions, and **incrementality** when the stakes justify a test. Do **not** turn a platform-vs-GA gap into a multiplier rule (Meta claims 2× GA ≠ "double Meta") — investigate windows and view-through, then ground any override in incrementality. 5. **Expect and budget for the gap.** Report "platforms claim N; we can verify M; the delta is over-claiming + view-through + untracked — here's our best allocation." 6. **Operationalize triangulation as rules when you can** — once you have a holdout or a trusted survey band for a channel, apply a documented **channel-level override** on reporting (shift credit from Direct/branded into the under-credited channel for the test window) rather than pretending raw MTA is truth. Details in `references/measurement-paradigms.md`. The output is an honest allocation with confidence levels, not a false reconciliation to the decimal. ### 6. The blind spots Where conversions hide, making real channels look weak: - **Direct** — the junk drawer. Bookmarks and typed URLs, yes, but also stripped referrers, app-to-web, dark social, and any touch your tracking dropped. A large direct share is a *measurement* problem, not a channel. - **Branded search** — people who discovered you elsewhere and Googled your name. Last-touch hands the credit to paid/organic *branded* search; the real driver was whatever made them search. Segment branded vs. non-branded or you'll defund the top of funnel. - **Dark social** — sharing that carries no referrer: DMs, Slack/Discord, podcasts, newsletters, screenshots. Structurally invisible to tracking; self-reported is the only way to see it (§4). - **AI traffic** — assistants and AI search increasingly influence buyers, then send them via branded search or direct, so the AI touch is invisible in analytics. Name it and hand deeper work to **ai-seo**. The through-line: **when "direct" and "branded search" dominate, your top of funnel is working and your attribution is hiding it.** Say that explicitly — it's the single most common misread in marketing. ### 7. Business-type fork Defaults differ sharply. Summary here; full playbooks in `references/by-business-type.md`. - **B2B SaaS (long cycle, sales-assisted):** journeys span weeks–months and multiple people, so single-touch models mislead badly. Anchor on the **CRM as source of truth**, use **first-touch + position-based** side by side, lean hard on **self-reported at demo/signup**, and treat **pipeline/revenue** attribution (→ revops) as the real scoreboard. Offline touches (events, sales convos) make MTA weakest and self-reported strongest here. - **Ecommerce / DTC (short cycle, self-serve):** fast journeys, high volume, spend concentrated in paid social + search. Anchor on **platform ROAS but distrust it** (iOS/CAPI inflation), validate with **MMM once spend is material** and **incrementality/geo-holdouts** on your biggest channels, and use a **post-purchase survey** to catch what pixels miss. Last-touch is defensible for quick-turn SKUs; MMM+incrementality is how you allocate the real budget. --- ## Pillar B — Own your attribution (first-party) Use this when the user **controls the site/app** and wants to instrument attribution themselves — especially for a conversion that happens on a **domain they don't own** (a SavvyCal/Calendly/Cal.com booking, a Stripe Checkout page). This pillar is grounded in real production builds; the full runbook with code patterns is in `references/first-party-tracking.md`. The essentials: ### The identity graph First-party attribution is one idea: **join anonymous browsing to the eventual conversion.** 1. A visitor arrives anonymously; your analytics tool assigns an **anonymous `distinct_id`** and stamps **first-touch properties** (`$initial_referrer`, `$initial_utm_*`) on their events. 2. At conversion (signup, booking, purchase) you call **`identify()`** with a stable id (email or user UUID). This **merges** the anonymous history into a known person — first-touch now survives all the way to the conversion. 3. Every conversion event can now be broken down by first-touch channel. That's the whole game. ### Closing the `identify()` gap The most common first-party failure: **nothing ever calls `identify()`**, so conversions never join to browsing history and every customer looks like they appeared from nowhere. (Framing adapted from Tessa Kriesel's PostHog approach.) The fix is to call identify at each real conversion. **Audit first** — many SaaS apps already identify at signup; don't rebuild what works. Find the *specific* un-instrumented conversions and close only those. ### Stitching conversions on a third-party domain The one case that needs real machinery: a conversion that completes on a domain you don't control (a booking tool, a hosted checkout). You can't run your analytics there, so: 1. **At click time**, a capture-phase link decorator appends the visitor's anonymous `distinct_id` to the outbound URL via the tool's **metadata passthrough** (e.g. `?metadata[ph_distinct_id]=<id>`). One document-level listener covers every CTA — no per-link edits. 2. The third-party tool stores that metadata and returns it in its **webhook**. 3. Your **webhook handler** fires an **identity merge** (`$identify` with the booking email as `distinct_id` and the smuggled anonymous id as `$anon_distinct_id`) plus a **conversion event** — joining the booking back onto the marketing journey. ### Guardrails (do not skip) - **Anonymity guard — fail closed.** Only ever smuggle the *anonymous* id. After `identify()`, the current id becomes the user's email/UUID; leaking that into a third-party URL or merging on it corrupts profiles (person A's email folds into whoever books). Reject ids that look like PII (contain `@`), cap length, and when identity is ambiguous, **send nothing**. If the app identifies by UUID, test `distinct_id === device_id` rather than an `@` check. - **First-touch data quality.** Redirects overwrite the true first touch. Exclude OAuth/checkout referrers (`accounts.google.com`, `checkout.stripe.com`, `login.*`), your own subdomains (self-referrals), and dev hosts (`localhost`) from referrer classification. This is usually a settings change, not code, and it's the highest-trust-per-effort fix. - **Cross-subdomain stitching.** Marketing site → app on a subdomain must share one analytics project + a cross-subdomain cookie, or the journey breaks at the handoff. Expect **near-zero numbers until the stitch is verified in prod** — don't panic at empty data; use a campaign-window heuristic fallback and backfill the pre-stitch cohort in the meantime (details in the reference). ### Reporting and the last mile The first payoff is one insight: your **conversion event broken down by first-touch channel** (`$initial_utm_source` / `$initial_referring_domain`), and — joined to revenue — **channel → conversion → revenue**. Confirm first-touch vs. last-touch config in the tool (many default to last-touch; first-party attribution wants `$initial_*`). But first-touch alone can't run the multi-touch models from §2. **Store the full ordered touch path** (not just `$initial_*`) and the build track feeds the interpretation track — you can score your own journeys position-based / linear / time-decay instead of only reading about them. **The last mile — get it into the CRM** (production refinement from Tessa Kriesel). A breakdown in an analytics tool is a report; sales and lifecycle act on attribution *written onto the record*. Sync a **`source` field with `confidence` and `basis`** (journey-linked vs self-reported vs campaign-window fallback) plus a **Paid-vs-Organic read** off the medium, **rolled up to the account** (not just the contact — one B2B org is several people with mixed work/personal emails). How pipeline/lifecycle then *use* it is **revops**' job. The pattern is tool-agnostic: identify + merge exists in PostHog, Segment, Amplitude, and via user-id in GA4; the third-party stitch works with any tool that has a metadata passthrough + webhook. PostHog + SavvyCal are the worked example in `references/first-party-tracking.md`. --- ## Output format Deliver an **attribution readout**, not a data dump: ```markdown # Attribution Readout — [date] ## The question [What decision this informs — e.g. "where should next quarter's budget go?"] ## Source of truth [Which system defines the conversion count, and why] ## What each source says | Channel | Platform-reported | GA | CRM | Self-reported | Our read | |---------|------------------|----|----|--------------|----------| [De-duped against source of truth; not summed] ## Model comparison (for long cycles) [First-touch vs last-touch side by side; the gap is the insight] ## Confidence & gaps [The attribution gap, the blind spots, what we can't see] ## Recommendation [Allocation call with confidence levels; the tiebreaker test worth running] ``` ## Tool Integrations For implementation, see the [tools registry](https://github.com/coreyhaines31/marketingskills/blob/main/tools/REGISTRY.md). Key tools: | Tool | Best For | MCP | Guide | |------|----------|:---:|-------| | **PostHog** | First-party attribution, identify/merge, funnels | - | [posthog.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/posthog.md) | | **GA4** | Web analytics, model comparison, user-id stitching | ✓ | [ga4.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/ga4.md) | | **Dub** | Short-link + click attribution | ✓ | [dub-co.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/dub-co.md) | | **Segment** | CDP — route identify/track to every destination | - | [segment.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/segment.md) | | **HubSpot** | CRM lead-source + self-reported fields | ✓ | [hubspot.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/hubspot.md) | | **Salesforce** | CRM as revenue source of truth | - | [salesforce.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/salesforce.md) | | **Supermetrics** | Pull platform numbers into one place to reconcile | ✓ | [supermetrics.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/supermetrics.md) | | **RB2B** | De-anonymize B2B website visitors | - | [rb2b.md](https://github.com/coreyhaines31/marketingskills/blob/main/tools/integrations/rb2b.md) | --- ## Related Skills - **analytics** — event tracking, tracking plans, UTMs, GA4/GTM setup. Do this *before* attribution. - **ads** — ad-platform pixels, CAPI, server-side conversion tracking (`references/conversion-tracking.md`). - **revops** — pipeline stages, lead lifecycle, CRM revenue reporting. Attribution feeds it. - **ai-seo** — the AI-search attribution blind spot in depth. - **ab-testing** — controlled experiments; the incrementality mindset applied to on-site changes.
Original skill: coreyhaines31/marketingskills/skills/attribution/SKILL.md Source: https://github.com/coreyhaines31/marketingskills/blob/dda3841f0b294e01e93b1541486beefbfab0915e/skills/attribution/SKILL.md License: MIT MIT License Copyright (c) 2025 Corey Haines Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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