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demo: retarget landing pages to the accounting audience

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Reorients the whole sales surface to accounting so it matches the rebuilt
demos. Replaces the Shopify and RevOps persona pages with accounts-payable
(1099) and accounts-receivable pages, refreshes the bookkeeper page, and
rewires the hub + deploy tooling:

- landing/bookkeeper/  — refreshed to the validated bank-rec demo
  (26 -> 20, six phantom duplicates), iframe ?p=bookkeeper.
- landing/ap-1099/     — NEW (replaces shopify-pet/): 1099 vendor prep,
  "24 records -> 8 vendors, 7 missing EINs recovered", iframe ?p=ap-1099,
  amber accent.
- landing/ar-aging/    — NEW (replaces revops/): AR open invoices,
  "26 -> 21, five double-entered invoices removed", iframe ?p=ar-aging,
  green accent.
- landing/index.html   — hub rewritten with the three accounting cards.
- deploy.py / deploy.config.example.json / README.md / _shared/styles.css
  — persona list, sitemap defaults, 404 links, cross-links, docs updated.

All demo iframes now point at the renamed app_demo personas; deploy.py
builds the dist bundle cleanly (verified) and the Gumroad ?from= tags match.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Michael
2026-06-22 18:59:50 +00:00
parent 6df726e69e
commit e7ec79b9b5
10 changed files with 867 additions and 841 deletions
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18
landing/README.md
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@@ -9,9 +9,9 @@ Cloudflare Pages.
```
landing/
├── _shared/styles.css shared CSS (system fonts, no externals)
├── shopify-pet/index.html Shopify operator (priority: pet supplies)
├── bookkeeper/index.html bookkeeper / freelance accountant
├── revops/index.html marketing / RevOps agency
├── bookkeeper/index.html bookkeeper — bank reconciliation
├── ap-1099/index.html accounts payable — 1099 vendor prep
├── ar-aging/index.html accounts receivable — open invoices
└── README.md this file
```
@@ -19,8 +19,8 @@ Each page:
- Inherits `landing/_shared/styles.css`
- Overrides the `--accent` colour variable in an inline `<style>` block
so each persona has its own visual identity (Shopify = mint green,
Bookkeeper = steel blue, RevOps = vivid violet)
so each persona has its own visual identity (Bookkeeper = steel blue,
AP / 1099 = amber/gold, AR = receivables green)
- Has a sticky buy bar with the Gumroad CTA tagged with `?from=<persona>`
- Embeds the live demo (Streamlit) via `<iframe>` with a sandbox attribute
- Carries persona-specific H1, sub-copy, use cases, FAQ, and a
@@ -64,13 +64,13 @@ wrangler pages deploy landing/dist
```
Configure the custom apex domain (`datatools.app`) in the Cloudflare
Pages project settings; sub-paths `/shopify-pet/`, `/bookkeeper/`,
`/revops/` are served automatically because the directory layout
Pages project settings; sub-paths `/bookkeeper/`, `/ap-1099/`,
`/ar-aging/` are served automatically because the directory layout
mirrors them. Cache rule defaults are fine (HTML 1 day, CSS 7 days).
If you want **separate Pages projects** per persona for independent
A/B testing, point three projects at the same `landing/dist/` and
configure each with its own sub-domain (`shopify.datatools.app`, etc.)
configure each with its own sub-domain (`bookkeeper.datatools.app`, etc.)
and a Pages rule that rewrites the root to that persona's
sub-directory.
@@ -110,7 +110,7 @@ Refresh the page when:
| `page_view → run_completed < 30%` for 4 weeks | The demo iframe isn't loading or visitors aren't engaging. Check the iframe URL. Move the demo above the fold if it's currently below. |
| New tool ships (06–09) | Add it to the persona's saved pipeline only if it fits — don't bloat the demo with every tool. |
| Pricing change | Update `<meta>` schema, the buybar `.price-tag`, the pricing card, and the FAQ. Search-and-replace `$49` across the file. |
| New persona added (4th, 5th) | Copy `shopify-pet/index.html`, replace persona-specific copy, add to the `footer` cross-link block on the existing pages. |
| New persona added (4th, 5th) | Copy `bookkeeper/index.html`, replace persona-specific copy, add to the `footer` cross-link block on the existing pages. |
## Why static HTML

4
landing/_shared/styles.css
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@@ -5,7 +5,7 @@
* with zero build step, no privacy banner needed).
* • Mobile-first; layout reflows below 720 px.
* • Dark, focused, content-first. Buyer reads this on a laptop
* between Shopify exports — keep it readable and skimmable.
* between messy accounting exports — keep it readable and skimmable.
* • Persona pages all share this sheet — niche differences live in
* copy + accent-color variables overridden in each page's <style>.
*/
@@ -18,7 +18,7 @@
--text-mute: #9aa3b2;
--text-soft: #c8ced8;
--rule: #252a36;
--accent: #6ee7b7; /* Shopify pet default — overridden per persona */
--accent: #6ee7b7; /* default accent — overridden per persona */
--accent-ink: #052e1a;
--warn: #fbbf24;
--max: 1080px;

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@@ -0,0 +1,391 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools for 1099 Prep — Clean Your Vendor Master & Recover Missing EINs Locally · $49</title>
<meta name="description" content="Build a clean 1099 vendor list — locally. Consolidates duplicate vendor rows, backfills scattered EINs, and flags the genuinely missing ones. 24 messy records → 8 complete vendors, 7 EINs recovered. Your data never leaves your computer. $49 one-time." />
<meta name="keywords" content="1099 vendor list, missing EIN, accounts payable cleanup, vendor master dedupe, 1099-NEC prep, QuickBooks vendor export, deduplicate vendors" />
<link rel="canonical" href="https://datatools.app/ap-1099/" />
<link rel="stylesheet" href="../_shared/styles.css" />
<!-- Persona accent: Accounts Payable / 1099 → amber/gold invoice tone -->
<style>
:root { --accent: #d97706; --accent-ink: #2a1604; }
</style>
<!-- Open Graph -->
<meta property="og:title" content="DataTools for 1099 Prep — Clean Your Vendor Master & Recover Missing EINs Locally" />
<meta property="og:description" content="Consolidate duplicate vendors, backfill scattered EINs, file 1099-NECs on time. Local. No upload. $49 one-time." />
<meta property="og:type" content="product" />
<meta property="og:url" content="https://datatools.app/ap-1099/" />
<!-- Schema.org Product -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "DataTools for 1099 Prep",
"operatingSystem": "Windows, macOS, Linux",
"applicationCategory": "BusinessApplication",
"offers": {
"@type": "Offer",
"price": "49",
"priceCurrency": "USD"
},
"description": "Clean your accounts-payable vendor master locally for 1099-NEC season. Six-tool data-cleaning bundle: dedupe-merge to consolidate duplicate vendor rows and backfill missing EINs, text-clean, format-standardize, missing-value handle, column-map, pipeline.",
"softwareVersion": "1.0"
}
</script>
</head>
<body>
<!-- ============= Sticky buy bar ============= -->
<div class="buybar">
<div class="buybar-inner">
<div class="brand"><span class="brand-mark">●</span> DataTools <span class="muted">/ for 1099 prep</span></div>
<div>
<span class="price-tag">$49 — one-time, no subscription</span>
<a class="btn" href="https://gumroad.com/l/datatools?from=ap-1099" rel="noopener">Get DataTools →</a>
</div>
</div>
</div>
<!-- ============= Hero ============= -->
<section class="hero">
<div class="container">
<div class="eyebrow">For accounts payable · 1099-NEC season · vendor master cleanup</div>
<h1>Build a clean 1099 vendor list —<br /><strong>with the missing EINs filled in.</strong></h1>
<p class="lead">
The same vendor got entered three times across the year — one row has
the EIN, another the address, another the phone — and now it's January
and you can't file because the numbers are scattered. DataTools
consolidates each vendor to one row and backfills the gaps from the
duplicates: in our sample, <strong>24 messy records become 8 complete
vendors with 7 missing EINs recovered</strong> from duplicate rows.
<strong>Your data never leaves your computer.</strong>
</p>
<div class="cta-row">
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ap-1099" rel="noopener">Get DataTools for Accounting — $49 →</a>
<a class="btn btn-ghost btn-large" href="#demo">Try the live demo ↓</a>
<span class="price-note">One-time payment · cross-platform · runs offline</span>
</div>
<div class="stats">
<div class="stat"><div class="num">24→8</div><div class="label">messy records to complete vendors</div></div>
<div class="stat"><div class="num">7</div><div class="label">missing EINs recovered</div></div>
<div class="stat"><div class="num">0</div><div class="label">cloud uploads ever</div></div>
</div>
</div>
</section>
<!-- ============= Pain points ============= -->
<section>
<div class="container">
<div class="eyebrow">If any of these sound like your January</div>
<h2>Five pains DataTools fixes in one pass</h2>
<div class="grid">
<div class="card">
<span class="icon">🧾</span>
<h3>The same vendor is in the list two or three times</h3>
<p>Different staff entered "Acme LLC", "Acme, L.L.C.", and "ACME Llc" across the year. Each is a separate row in the vendor master, and each only holds part of the story — so your 1099 totals split across three near-duplicate spellings.</p>
<p class="muted"><strong>What it costs:</strong> hours of manual matching, plus the risk of filing the wrong total.</p>
</div>
<div class="card">
<span class="icon">🔢</span>
<h3>The EIN is on a different row than the rest of the details</h3>
<p>One record captured the EIN at onboarding; the row you actually paid against doesn't have it. At 1099 time the field is blank even though you collected it months ago — it's just sitting on a duplicate.</p>
<p class="muted"><strong>What it costs:</strong> chasing W-9s you already have on file.</p>
</div>
<div class="card">
<span class="icon">📵</span>
<h3>Phones, addresses, and amounts are formatted five different ways</h3>
<p>Remittance phone as <code>(212) 555-0147</code> on one row and <code>212.555.0147</code> on another. Amounts with stray <code>$</code> and commas. The export won't reconcile and the 1099-NEC box totals don't tie out.</p>
<p class="muted"><strong>What it costs:</strong> a half-day reconciling before you can even start filing.</p>
</div>
<div class="card">
<span class="icon">⛔</span>
<h3>You don't know which EINs are genuinely missing</h3>
<p>Some EINs are recoverable from a duplicate row. Some you never collected. Until the list is consolidated you can't tell the two apart — so you either over-chase vendors or under-file.</p>
<p class="muted"><strong>What it costs:</strong> late filings and TIN-mismatch penalties.</p>
</div>
<div class="card">
<span class="icon">📤</span>
<h3>Your QuickBooks vendor export doesn't match your AP ledger</h3>
<p>The vendor master in QuickBooks, the payments spreadsheet, and the W-9 tracker each use different column names for "vendor name" / "Tax ID" / "amount paid." Merging them is an afternoon of manual rename before any analysis begins.</p>
<p class="muted"><strong>What it costs:</strong> 4–8 hours per filing season manually merging exports.</p>
</div>
<div class="card">
<span class="icon">🔒</span>
<h3>Cloud cleaners want you to upload your vendor master</h3>
<p>Your vendor master holds EINs, remittance addresses, and payment history — exactly the data you should not be uploading to a SaaS to clean. DataTools is desktop-only — your vendor list never leaves your computer.</p>
<p class="muted"><strong>What it costs:</strong> nothing — and that's the point.</p>
</div>
</div>
</div>
</section>
<!-- ============= Live demo ============= -->
<section id="demo">
<div class="container">
<div class="eyebrow">Live demo · runs in your browser</div>
<h2>Try it on a real-looking vendor master export</h2>
<p>
The demo below loads a sample 24-row vendor file with the pollution
we've seen in real AP systems: the same vendor entered two or three
times under slightly different spellings, EINs that live on one
duplicate row but not the one you paid against, phones and amounts
formatted five ways, and the usual mess of
<code>N/A</code> / <code>(blank)</code> / <code>?</code> sentinels.
Click <strong>Run pipeline</strong> and watch the 24 records collapse
to <strong>8 complete vendors with 7 EINs recovered</strong> in under
a second.
</p>
<div class="demo-frame">
<iframe
src="https://demo.datatools.app/?p=ap-1099"
loading="lazy"
title="DataTools live demo — accounts payable / 1099 vendor cleanup"
sandbox="allow-scripts allow-same-origin allow-downloads allow-forms"></iframe>
<div class="demo-caption">
Demo runs on free hosting (Streamlit Community Cloud). Capped at
100 input rows · output watermarked with one trailing row. The
paid product has no caps and runs entirely offline.
</div>
</div>
</div>
</section>
<!-- ============= Built for AP / 1099 ============= -->
<section>
<div class="container">
<div class="eyebrow">Built for the accounts-payable team</div>
<h2>Five workflows you do every filing season</h2>
<div class="grid">
<div class="card">
<span class="icon">🧹</span>
<h3>Vendor-master consolidation</h3>
<p>Catches the same vendor that shows up as <code>Acme LLC</code>, <code>Acme, L.L.C.</code>, and <code>ACME Llc</code>. Fuzzy match merges the spellings; the dedup merge collapses them to one row and backfills the gaps from each duplicate.</p>
</div>
<div class="card">
<span class="icon">🔢</span>
<h3>EIN backfill &amp; missing-EIN flagging</h3>
<p>Pulls the EIN off whichever duplicate row captured it and fills it into the survivor. The EINs that are <em>genuinely</em> missing get flagged so you know exactly which W-9s to chase.</p>
</div>
<div class="card">
<span class="icon">💵</span>
<h3>1099-NEC amount roll-up</h3>
<p>Before filing: standardize amounts, drop sentinels-as-missing, and merge so each vendor's total paid lands on one row and ties to your AP ledger.</p>
</div>
<div class="card">
<span class="icon">📥</span>
<h3>QuickBooks vendor export cleanup</h3>
<p>Whitespace in Tax IDs, near-identical vendor names, copy-paste smart quotes in remittance addresses — gone. Audit log shows every change for your reviewer.</p>
</div>
<div class="card">
<span class="icon">🔗</span>
<h3>Merging the W-9 tracker into the AP ledger</h3>
<p>The vendor master, the payments spreadsheet, and the W-9 tracker each name "Tax ID" differently. Map Columns aligns them; the dedup merge consolidates across all three sources.</p>
</div>
<div class="card">
<span class="icon">⚙️</span>
<h3>Repeatable pipeline</h3>
<p>Save the cleanup as a JSON file. Drop next year's vendor export on it. Same consolidation, zero re-configuration. Automatable via the CLI.</p>
</div>
</div>
</div>
</section>
<!-- ============= Privacy moat ============= -->
<section>
<div class="container">
<div class="eyebrow">The thing every cloud cleaner can't say</div>
<h2>Your vendor master never leaves your computer.</h2>
<p>
DataTools is a desktop app. There's no upload step, no SaaS account,
no subscription, no "trust our security policy." The first thing you
can do after install is open your browser's network tab, run the
cleaner on your real vendor file, and verify zero outbound
requests.
</p>
<div class="callout">
<strong>Why it matters for AP:</strong> your vendor master holds EINs,
remittance addresses, and payment history. Cloud cleaners require you
to upload it. We don't.
</div>
<div class="terminal"><span class="prompt">$</span> python -m src.cli_pipeline vendor_1099.csv --pipeline vendor_1099_pipeline.json --apply
Reading vendor_1099.csv...
24 rows, 9 columns
Executing pipeline:
<span class="ok">✓</span> text_clean (38 ms) {cells_changed: 41}
<span class="ok">✓</span> format_standardize (62 ms) {cells_changed: 36} # phones, EINs, amounts
<span class="ok">✓</span> missing (11 ms) {sentinels_standardized: 9}
<span class="ok">✓</span> dedup (140 ms) {groups_merged: 8, rows_removed: 16, eins_backfilled: 7}
Initial rows: 24 → Final rows: 8 (8 complete vendors)
EINs recovered from duplicate rows: 7 | Still missing (flagged): 1
Unparseable cells: 0
Total elapsed: 0.25 s
<span class="prompt">$</span> # zero network calls. zero. promise.</div>
</div>
</section>
<!-- ============= Audit moat ============= -->
<section>
<div class="container">
<div class="eyebrow">For when your reviewer asks "what changed?"</div>
<h2>Every change auditable. Every cell logged.</h2>
<p>
Every modification is recorded with the original value, the new
value, and which rule fired. Hand the audit CSV to your controller,
your reviewer, or the IRS-ready workpaper file along with the cleaned
vendor list. No <em>"I trust the AI"</em> hand-waving — they see
exactly which EIN came from which duplicate row.
</p>
<div class="callout">
<strong>Real example:</strong> the demo above merged 24 records into
8 vendors and backfilled 7 EINs. The dedup audit lists every vendor
group with the survivor, its merged-in duplicates, and the source row
each recovered EIN was pulled from. The standardize audit lists every
phone, amount, and Tax ID it reformatted.
</div>
</div>
</section>
<!-- ============= Format handling ============= -->
<section>
<div class="container">
<div class="eyebrow">If your vendors are messy — most AP files are</div>
<h2>EINs, phones, addresses, and amounts in every shape.</h2>
<p>
One row has the EIN as <code>12-3456789</code>, another as
<code>123456789</code>. The remittance phone is <code>(212)
555-0147</code> on one and <code>212.555.0147</code> on the next.
An amount reads <code>$12,410.75</code> with a stray space. Excel
treats half of these as text errors. DataTools normalizes every one —
EINs to a single format, phones to E.164, amounts to clean numerics —
so the file reconciles and the 1099 box totals tie out.
</p>
<ul class="bullets">
<li><strong>EIN / Tax-ID normalization</strong> to one consistent <code>NN-NNNNNNN</code> shape, with genuinely-missing ones flagged.</li>
<li><strong>Phone standardization</strong> to E.164 via Google's libphonenumber.</li>
<li><strong>Amount parsing</strong> for <code>$</code> / commas / stray spaces — including amounts Excel mis-types as text.</li>
<li><strong>Address shape detection</strong> for US remittance addresses.</li>
</ul>
</div>
</section>
<!-- ============= What you get ============= -->
<section>
<div class="container">
<div class="eyebrow">In the bundle</div>
<h2>Six tools. One pipeline. One $49 download.</h2>
<div class="grid">
<div class="card"><h3>1 · Find Duplicates</h3><p>Fuzzy match (Jaro-Winkler), 5 normalizers, survivor rules, gap-backfill merge, interactive review.</p></div>
<div class="card"><h3>2 · Clean Text</h3><p>Whitespace, smart chars, NBSP, BOM, line endings, case ops.</p></div>
<div class="card"><h3>3 · Standardize Formats</h3><p>EINs, amounts, dates, phones, emails, addresses, names, booleans.</p></div>
<div class="card"><h3>4 · Fix Missing Values</h3><p>Disguised-null detection, profile, flag genuinely-missing fields, drop strategies.</p></div>
<div class="card"><h3>5 · Map Columns</h3><p>Fuzzy auto-rename, target schema, type coercion, required-field defaults.</p></div>
<div class="card"><h3>6 · Automated Workflows</h3><p>Chain tools in recommended order, save/load JSON, automate next year's vendor cleanup.</p></div>
</div>
</div>
</section>
<!-- ============= Pricing ============= -->
<section>
<div class="container">
<div class="eyebrow">Pricing — pay once, own it</div>
<h2>$49. No subscription. No ceiling on rows or files.</h2>
<div class="pricing">
<div class="card featured">
<div class="row"><div class="price">$49</div><div class="price-suffix">one-time</div></div>
<h3>DataTools for 1099 Prep</h3>
<ul>
<li>All 6 tools, full pipeline</li>
<li>Mac · Windows · Linux installers</li>
<li>Code-signed (no Gatekeeper warnings)</li>
<li>Free updates for the v1.x line</li>
<li>Bonus: ready-made vendor-master &amp; 1099 pipelines</li>
</ul>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ap-1099" rel="noopener">Buy on Gumroad →</a>
</div>
<div class="card">
<div class="row"><div class="price">$149</div><div class="price-suffix">one-time</div></div>
<h3>Full DataTools Suite</h3>
<p class="muted">Available when 3+ bundles ship. Includes everything in the 1099-prep pack plus the Bookkeeper and Accounts-Receivable bundles. Save $48.</p>
<a class="btn btn-ghost btn-large" href="#" aria-disabled="true">Coming when ready</a>
</div>
</div>
</div>
</section>
<!-- ============= FAQ ============= -->
<section>
<div class="container">
<h2>Questions</h2>
<details class="faq">
<summary>Does this work with my QuickBooks vendor export?</summary>
<p>Yes — the input is just CSV / Excel from any source. Your QuickBooks vendor export works the same as a Xero export, a Bill.com download, or a vendor spreadsheet you maintain by hand. The cleaner doesn't care where the file came from.</p>
</details>
<details class="faq">
<summary>How does this compare to Excel's "Remove Duplicates"?</summary>
<p>Excel does <em>exact</em> deduplication and only deletes — it never backfills. <code>Acme LLC</code> and <code>Acme, L.L.C.</code> are different vendors to Excel, and even when it does catch a duplicate it throws the extra row away, taking the EIN with it. DataTools fuzzy-matches across spelling drift, merges the group to one survivor, and pulls the missing EIN, phone, and address off the rows it merges in.</p>
</details>
<details class="faq">
<summary>How does it recover a missing EIN?</summary>
<p>When it merges a group of duplicate vendor rows, it keeps the survivor and backfills any empty field — including the EIN — from whichever duplicate row had it. In the sample file, 7 of the 8 vendors had their EIN recovered this way; the 1 that's truly missing gets flagged so you know to chase the W-9.</p>
</details>
<details class="faq">
<summary>Do I need to know Python to use it?</summary>
<p>No. The GUI is a browser interface that opens automatically when you double-click the app. It loads your vendor file, you click Run, you download the cleaned list. The CLI is there for power users who want to script next year's cleanup.</p>
</details>
<details class="faq">
<summary>What about my data privacy?</summary>
<p>Your vendor master — EINs, remittance addresses, payment history — never leaves your computer. There is no cloud component, no telemetry, no "anonymous usage stats." When the app is running you can confirm zero outbound network requests in your browser's developer tools.</p>
</details>
<details class="faq">
<summary>What's your refund policy?</summary>
<p>Try the live demo above on the sample vendor dataset before you buy. If you still find DataTools doesn't fit your workflow within 14 days, email for a refund — no questions asked.</p>
</details>
<details class="faq">
<summary>Will there be updates?</summary>
<p>Yes. The v1.x line is included free for everyone who buys DataTools today. We ship a patch every 30 days adding format support, edge-case fixes, and small features.</p>
</details>
</div>
</section>
<!-- ============= Final CTA ============= -->
<section>
<div class="container" style="text-align: center;">
<h2>Stop chasing scattered EINs by hand.</h2>
<p class="lead" style="margin: 0 auto 28px;">One $49 download. Mac, Windows, or Linux. Runs offline. Consolidates 24 messy records into 8 complete vendors, recovers the 7 EINs hiding on duplicate rows, flags the ones genuinely missing, and saves a pipeline you can re-run on next year's vendor export.</p>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ap-1099" rel="noopener">Get DataTools for Accounting — $49 →</a>
</div>
</section>
<!-- ============= Footer ============= -->
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for accounts payable, bookkeepers, and accounts-receivable teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="../bookkeeper/">For bookkeepers</a> ·
<a href="../ar-aging/">For accounts receivable</a><br />
<a href="https://gumroad.com/l/datatools?from=ap-1099">Buy on Gumroad</a> ·
<a href="mailto:hello@datatools.app">Email support</a>
</p>
</div>
</div>
</footer>
</body>
</html>

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools for Accounts Receivable — Kill Duplicate Invoices Inflating Your AR Aging Report · $49</title>
<meta name="description" content="One tool to clean your open-invoices export: standardize invoice dates, due dates, and amounts, lowercase client emails, then remove double-entered invoice numbers so your AR aging report is accurate. 26 rows → 21, five duplicate invoices removed. Fully offline. $49 one-time." />
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<div class="brand"><span class="brand-mark">●</span> DataTools <span class="muted">/ for Accounts Receivable</span></div>
<div>
<span class="price-tag">$49 — one-time, no subscription</span>
<a class="btn" href="https://gumroad.com/l/datatools?from=ar-aging" rel="noopener">Get DataTools →</a>
</div>
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<section class="hero">
<div class="container">
<div class="eyebrow">For accounts receivable · controllers · collections · accounting teams</div>
<h1>Stop chasing the invoices<br /><strong>your aging report counted twice.</strong></h1>
<p class="lead">
The same invoice number gets posted twice — once as
<code>3/04/2026</code> for <code>$1,250.00</code>, again as
<code>2026-03-04</code> for <code>1250</code> — so your AR aging
report double-counts the receivable and your team chases a balance
that was never really open. DataTools standardizes every invoice
date, due date, and amount, lowercases client emails, then removes
the double-entered invoice numbers — taking a real open-invoices
export from <strong>26 rows to 21, five duplicate invoices
removed</strong> — all on your own machine, with nothing uploaded.
</p>
<div class="cta-row">
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ar-aging" rel="noopener">Get DataTools for Accounting — $49 →</a>
<a class="btn btn-ghost btn-large" href="#demo">Try the live demo ↓</a>
<span class="price-note">One-time payment · cross-platform · runs offline</span>
</div>
<div class="stats">
<div class="stat"><div class="num">26→21</div><div class="label">rows after dedupe</div></div>
<div class="stat"><div class="num">5</div><div class="label">duplicate invoices removed</div></div>
<div class="stat"><div class="num">0</div><div class="label">cloud uploads ever</div></div>
</div>
</div>
</section>
<!-- ============= Pain points ============= -->
<section>
<div class="container">
<div class="eyebrow">If your last aging report didn't tie out to cash</div>
<h2>Five pains DataTools fixes before you run the aging report</h2>
<div class="grid">
<div class="card">
<span class="icon">💸</span>
<h3>Double-entered invoices inflate every aging bucket</h3>
<p>The same invoice number posted twice — once in <code>MM/DD/YYYY</code>, once in ISO — lands in two rows and gets counted twice. Your 60-day bucket looks worse than it is, and the receivables total overstates what's actually owed.</p>
<p class="muted"><strong>What it costs:</strong> overstated AR, a balance sheet that won't reconcile, and a controller asking why.</p>
</div>
<div class="card">
<span class="icon">📞</span>
<h3>Collections chases invoices that were already paid or never real</h3>
<p>When a duplicate invoice number shows as still-open, a collector emails the client about a balance that doesn't exist. The client pushes back, trust erodes, and your team burns a morning untangling it.</p>
<p class="muted"><strong>What it costs:</strong> wasted collections hours + an awkward "please disregard" to the client.</p>
</div>
<div class="card">
<span class="icon">⚖️</span>
<h3>Uploading the AR ledger to a cloud cleaner is a compliance headache</h3>
<p>Every cloud-based cleaner wants you to upload your full receivables ledger — client names, amounts, contact emails. That's a data-handling review your firm doesn't want to run. DataTools is desktop-only — no upload, no DPA, no review.</p>
<p class="muted"><strong>What it costs:</strong> weeks of review per tool, or just not cleaning the data at all.</p>
</div>
<div class="card">
<span class="icon">🗓️</span>
<h3>Mixed date formats make due dates and aging unreliable</h3>
<p>Invoice dates arrive as <code>3/4/26</code>, <code>2026-03-04</code>, and <code>Mar 4 2026</code>; due dates are just as mixed. Sort by date and the buckets are wrong, so the wrong invoices show up in the wrong aging column.</p>
<p class="muted"><strong>What it costs:</strong> 1–3 hours per close reconciling dates by hand, every period.</p>
</div>
<div class="card">
<span class="icon">📧</span>
<h3>Messy client contacts break your remittance reminders</h3>
<p>Client names come in mixed casing and emails arrive as <code>Billing@ClientCo.com</code> in one row and <code>billing@clientco.com</code> in another — so the same client looks like two, and reminders go out twice or not at all.</p>
<p class="muted"><strong>What it costs:</strong> duplicate dunning, missed reminders, and a client list that won't group.</p>
</div>
<div class="card">
<span class="icon">❓</span>
<h3>Blank invoice statuses hide whether a receivable is really open</h3>
<p>When one of the two twin rows has a blank status, you can't tell if the invoice is open, partial, or paid — so it either gets dropped from the aging report or counted at the wrong stage.</p>
<p class="muted"><strong>What it costs:</strong> misclassified receivables and an aging report you can't trust.</p>
</div>
</div>
</div>
</section>
<section id="demo">
<div class="container">
<div class="eyebrow">Live demo · runs in your browser</div>
<h2>Try it on a real-looking open-invoices export</h2>
<p>
The demo below loads a 26-row open-invoices export with five
double-entered invoice numbers — the same invoice posted twice in
different date and amount formats (<code>3/04/2026</code> vs
<code>2026-03-04</code>, <code>$1,250.00</code> vs <code>1250</code>),
client emails in mixed case, and one blank invoice status. Click
<strong>Run pipeline</strong> and watch the 5-step pipeline (text
clean → format → missing → column map → dedup) standardize both date
columns to ISO, coerce amounts to numbers, lowercase the emails, and
collapse 26 rows to 21 — backfilling the blank status from its twin
row so the aging report is accurate.
</p>
<div class="demo-frame">
<iframe
src="https://demo.datatools.app/?p=ar-aging"
loading="lazy"
title="DataTools live demo — Accounts Receivable"
sandbox="allow-scripts allow-same-origin allow-downloads allow-forms"></iframe>
<div class="demo-caption">
Demo runs on free hosting. Capped at 100 input rows · output
watermarked. The paid product has no caps and runs entirely offline.
</div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">Built for the receivables close</div>
<h2>Three workflows you do every period</h2>
<div class="grid">
<div class="card">
<span class="icon">🪢</span>
<h3>Dedupe double-entered invoices</h3>
<p>Match on invoice number, drop the second posting, and keep one canonical row per invoice — backfilling a blank status, due date, or amount from its twin so nothing accurate is lost when the duplicate goes.</p>
</div>
<div class="card">
<span class="icon">🗓️</span>
<h3>Standardize invoice and due dates</h3>
<p>Coerce every invoice date and due date to ISO and every amount to a clean number, so the aging buckets sort correctly and the receivables total ties out to the ledger.</p>
</div>
<div class="card">
<span class="icon">📧</span>
<h3>Normalize client contacts for remittance</h3>
<p>Lowercase client emails and fix name casing so each client groups as one. Send remit-to reminders once, to a clean contact list — not twice because two rows looked like two clients.</p>
</div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">If your export comes from QuickBooks, Xero, or a billing system</div>
<h2>Standardized dates and amounts. One row per invoice.</h2>
<p>
Your billing system exports <code>3/04/2026</code>. The re-post of
the same invoice has <code>2026-03-04</code>. The amount is
<code>$1,250.00</code> in one row and <code>1250</code> in the other.
DataTools reads each row, normalizes both date columns to ISO,
coerces the amount to a number, and then matches on invoice number
to keep exactly one canonical row per receivable.
</p>
<ul class="bullets">
<li><strong>Invoice date + due date</strong> both standardized to ISO, so every aging bucket sorts and totals correctly.</li>
<li><strong>Amounts coerced to numbers</strong>: <code>$1,250.00</code> and <code>1250</code> resolve to the same value — no false mismatch between twin rows.</li>
<li><strong>Client emails lowercased</strong> so the same client groups as one for remittance reminders.</li>
<li><strong>Status backfill on dedupe</strong>: when a twin row has a blank invoice status, the survivor inherits it — so no open receivable goes missing from the report.</li>
</ul>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">For anyone who reports on receivables</div>
<h2>Every duplicate invoice you don't catch overstates your AR.</h2>
<p>
Your aging report is only as good as the export under it. Every
double-entered invoice number is a receivable counted twice — it
inflates the aging buckets, overstates the total owed, and sends
collections after balances that aren't really open. DataTools
catches them once, before the report runs, by matching on invoice
number with the date and amount noise already standardized away.
</p>
<div class="callout">
<strong>Real numbers from the demo:</strong> a 26-row open-invoices
export collapses to 21 — that's five double-entered invoices the
mixed date and amount formats were hiding, both date columns now
ISO, amounts numeric, emails lowercased, 0 unparseable, and a blank
status backfilled from its twin row. The aging report finally ties out.
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">The thing every cloud cleaner can't say</div>
<h2>Your clients' receivables never leave your computer.</h2>
<p>
Cloud cleaning tools require you to upload your AR ledger — client
names, invoice amounts, remit-to contacts. That ledger is sensitive
client financial data, and once it's on someone else's server, your
firm owns a data-handling problem you didn't need. DataTools is a
desktop app. There is no upload step.
</p>
<div class="terminal"><span class="prompt">$</span> python -m src.cli_pipeline ar_open_invoices.csv --pipeline ar_open_invoices_pipeline.json --apply
Reading ar_open_invoices.csv...
26 rows, 9 columns
Executing pipeline:
<span class="ok">✓</span> text_clean (40 ms) {cells_changed: 31}
<span class="ok">✓</span> format_standardize (120 ms) {dates_to_iso: 41, amounts_to_number: 26, emails_lowercased: 18}
<span class="ok">✓</span> missing (30 ms) {sentinels_standardized: 4, status_backfilled: 1}
<span class="ok">✓</span> column_map (20 ms) {columns_renamed: 2}
<span class="ok">✓</span> dedup (60 ms) {duplicate_invoices_removed: 5, merged: 5}
Initial rows: 26 → Final rows: 21
Unparseable dates/amounts: 0
Total elapsed: 0.3 s
<span class="prompt">$</span> # 5 double-entered invoices gone. aging report ties out. for $49.</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">In the bundle</div>
<h2>Six tools. One pipeline. One $49 download.</h2>
<div class="grid">
<div class="card"><h3>1 · Find Duplicates</h3><p>Match on invoice number; keep one canonical row per receivable and backfill blanks from the twin.</p></div>
<div class="card"><h3>2 · Clean Text</h3><p>Smart quotes from copy-paste, NBSP from spreadsheet exports, BOM from Excel.</p></div>
<div class="card"><h3>3 · Standardize Formats</h3><p>Invoice and due dates to ISO, amounts to clean numbers, client emails lowercased.</p></div>
<div class="card"><h3>4 · Fix Missing Values</h3><p>Detect <code>TBD</code>, <code>(unknown)</code>, <code>—</code> and backfill blank invoice statuses on dedupe.</p></div>
<div class="card"><h3>5 · Map Columns</h3><p>Project to your aging-report schema, coerce amount to a number, reorder fields for import.</p></div>
<div class="card"><h3>6 · Automated Workflows</h3><p>Save the cleanup as JSON. Drop next period's open-invoices export on it. Same dedupe, automated.</p></div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">Pricing — pay once, own it</div>
<h2>$49. No subscription. No per-close fee.</h2>
<div class="pricing">
<div class="card featured">
<div class="row"><div class="price">$49</div><div class="price-suffix">one-time</div></div>
<h3>DataTools for Accounts Receivable</h3>
<ul>
<li>All 6 tools, full pipeline</li>
<li>Mac · Windows · Linux installers</li>
<li>Code-signed (no Gatekeeper warnings)</li>
<li>Free updates for the v1.x line</li>
<li>Bonus: open-invoices dedupe pipeline preset</li>
<li><strong>Use on any number of clients</strong> — no seat limits</li>
</ul>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ar-aging" rel="noopener">Buy on Gumroad →</a>
</div>
<div class="card">
<div class="row"><div class="price">$149</div><div class="price-suffix">one-time</div></div>
<h3>Full DataTools Suite</h3>
<p class="muted">Available when 3+ bundles ship. Includes everything in the Accounts Receivable pack plus the Bookkeeper and Accounts Payable / 1099 bundles. Save $48.</p>
<a class="btn btn-ghost btn-large" href="#" aria-disabled="true">Coming when ready</a>
</div>
</div>
</div>
</section>
<section>
<div class="container">
<h2>Questions</h2>
<details class="faq">
<summary>Does this replace my accounting system's deduplication?</summary>
<p>No — it cleans the export <em>before</em> you run the aging report or import it back. Most billing systems will happily hold two postings of the same invoice number; DataTools catches the double-entered invoice so it never inflates a single aging bucket.</p>
</details>
<details class="faq">
<summary>How does it know two rows are the same invoice?</summary>
<p>It matches on invoice number after the date and amount formats are standardized away. So a posting dated <code>3/04/2026</code> for <code>$1,250.00</code> and its twin dated <code>2026-03-04</code> for <code>1250</code> are recognized as one invoice — and only one canonical row survives.</p>
</details>
<details class="faq">
<summary>What happens to a blank invoice status when the duplicate is removed?</summary>
<p>It's backfilled. If one twin row has a blank status and the other says <code>open</code>, the surviving row inherits <code>open</code> — so no real receivable drops off the aging report just because the duplicate carried the better data.</p>
</details>
<details class="faq">
<summary>Can I use it on multiple clients without paying again?</summary>
<p>Yes. The licence is per-operator, not per-client. Run it on every client's open-invoices export for the same $49.</p>
</details>
<details class="faq">
<summary>What's the audit trail look like?</summary>
<p>A row-by-row CSV: every modified cell with its original value, new value, and which rule fired — every date coerced to ISO, every amount normalized, every duplicate invoice removed. A separate JSON file describes the pipeline that produced it, so the cleanup reproduces deterministically and your client can verify it on their machine.</p>
</details>
<details class="faq">
<summary>What's your refund policy?</summary>
<p>Try the live demo above on the sample open-invoices export before you buy. If DataTools doesn't fit your workflow within 14 days, email for a refund — no questions asked.</p>
</details>
</div>
</section>
<section>
<div class="container" style="text-align: center;">
<h2>Stop counting the same receivable twice.</h2>
<p class="lead" style="margin: 0 auto 28px;">One $49 download. Standardizes invoice dates, due dates, and amounts, lowercases client emails, removes the double-entered invoices your aging report was counting twice, and saves a pipeline you can re-run on next period's open-invoices export.</p>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=ar-aging" rel="noopener">Get DataTools for Accounting — $49 →</a>
</div>
</section>
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for bookkeepers, accounts payable, and accounts receivable teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="../bookkeeper/">For bookkeepers</a> ·
<a href="../ap-1099/">For accounts payable / 1099</a><br />
<a href="https://gumroad.com/l/datatools?from=ar-aging">Buy on Gumroad</a> ·
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<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools for Bookkeepers — Reconcile Bank Exports With An Audit Trail · $49</title>
<meta name="description" content="Reconcile messy bank exports. Catch duplicate transactions QuickBooks imported twice. Standardize dates, amounts, and vendor casing — locally. Every change auditable. $49 one-time." />
<meta name="keywords" content="reconcile bank export csv, quickbooks duplicate transactions, vendor list cleanup, bookkeeper csv tool, bank export deduplicator, bookkeeper audit trail" />
<title>DataTools for Bookkeepers — Catch Bank Transactions Posted Twice · $49</title>
<meta name="description" content="Catch the transactions your bank export posted twice. Standardize every date to ISO and every amount to numeric, then dedup on the real transaction so the reconciliation ties out — with a row-level audit trail. $49 one-time." />
<meta name="keywords" content="bank reconciliation, duplicate transactions, bank export csv cleanup, QuickBooks reconcile, bookkeeper csv tool" />
<link rel="canonical" href="https://datatools.app/bookkeeper/" />
<link rel="stylesheet" href="../_shared/styles.css" />
@@ -18,8 +18,8 @@
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<meta property="og:title" content="DataTools for Bookkeepers — Reconcile Bank Exports With An Audit Trail" />
<meta property="og:description" content="Catch duplicate transactions. Standardize dates and amounts. Hand your client an audit trail. $49 one-time." />
<meta property="og:title" content="DataTools for Bookkeepers — Catch Bank Transactions Posted Twice" />
<meta property="og:description" content="The same payment posts twice in two date/amount formats and a plain dedupe misses it. DataTools standardizes, dedups on the real transaction, and hands you an audit trail. $49 one-time." />
<meta property="og:type" content="product" />
<meta property="og:url" content="https://datatools.app/bookkeeper/" />
@@ -35,7 +35,7 @@
"price": "49",
"priceCurrency": "USD"
},
"description": "Reconcile bank exports, dedupe vendor lists, and produce a hand-off-ready audit trail. Six-tool data-cleaning bundle for bookkeepers and freelance accountants.",
"description": "Catch the duplicate transactions your bank export posted twice across overlapping months, standardize dates and amounts, and produce a hand-off-ready audit trail. Six-tool data-cleaning bundle for bookkeepers and freelance accountants.",
"softwareVersion": "1.0"
}
</script>
@@ -47,7 +47,7 @@
<div class="brand"><span class="brand-mark">●</span> DataTools <span class="muted">/ for Bookkeepers</span></div>
<div>
<span class="price-tag">$49 — one-time, no subscription</span>
<a class="btn" href="https://gumroad.com/l/datatools?from=bookkeeper" rel="noopener">Get DataTools →</a>
<a class="btn" href="https://gumroad.com/l/datatools?from=bookkeeper" rel="noopener">Get DataTools for Bookkeepers — $49 →</a>
</div>
</div>
</div>
@@ -55,24 +55,29 @@
<section class="hero">
<div class="container">
<div class="eyebrow">For bookkeepers · freelance accountants · small-firm partners</div>
<h1>Reconcile messy bank exports.<br /><strong>Hand your client an audit trail.</strong></h1>
<h1>Catch the transactions your bank export<br /><strong>posted twice.</strong></h1>
<p class="lead">
The Jan and Feb exports overlap and you've got the same transaction
booked twice. Vendor names are <em>"Amazon"</em>, <em>"amazon.com"</em>,
and <em>"AMAZON.COM*4F2X9"</em> in three different rows. Dates are a
smoosh of <code>01/15/2025</code>, <code>2025-01-15</code>, and
<code>Jan 18 2025</code>. DataTools fixes all of it in one pass —
and produces a row-by-row CSV showing every change so your client
can verify your work.
The Jan and Feb exports overlap, so the <em>same</em> payment posts
twice in two different shapes — <code>01/15/2025&nbsp;&nbsp;+$3,450.00</code>
in one export and <code>2025-01-15&nbsp;&nbsp;3450.00</code> in the
other — and a plain Excel dedupe never catches it because the dates and
amounts don't match character-for-character. DataTools standardizes
every date to ISO and every amount to numeric (parens-negatives
resolved), then dedups on the <em>real</em> transaction so the
reconciliation ties out. On the sample export that's
<strong>26 rows → 20</strong> — six phantom duplicate transactions
removed, 36 date/amount cells standardized, 0 unparseable — and you
get a row-by-row CSV showing every change so your client can verify
your work.
</p>
<div class="cta-row">
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=bookkeeper" rel="noopener">Get DataTools — $49 →</a>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=bookkeeper" rel="noopener">Get DataTools for Bookkeepers — $49 →</a>
<a class="btn btn-ghost btn-large" href="#demo">Try the live demo ↓</a>
<span class="price-note">One-time payment · cross-platform · runs offline</span>
</div>
<div class="stats">
<div class="stat"><div class="num">6</div><div class="label">tools, one bundle</div></div>
<div class="stat"><div class="num">100 %</div><div class="label">auditable changes</div></div>
<div class="stat"><div class="num">26→20</div><div class="label">rows, on the sample export</div></div>
<div class="stat"><div class="num">6</div><div class="label">phantom duplicates removed</div></div>
<div class="stat"><div class="num">0</div><div class="label">cloud uploads ever</div></div>
</div>
</div>
@@ -129,13 +134,15 @@
<div class="eyebrow">Live demo · runs in your browser</div>
<h2>Try it on a sample bank export with a known overlap</h2>
<p>
The demo below loads a 25-row export combining January and February
The demo below loads a 26-row export combining January and February
activity, with the month-boundary rows duplicated across exports —
the exact scenario where QuickBooks (or any reconciler) silently
double-counts transactions. Click <strong>Run pipeline</strong> and
watch the dedup catch every overlap, dates land in ISO format, and
the parens-negative amounts (<code>($89.50)</code>) become proper
negative numbers.
watch it standardize 36 date/amount cells, land every date in ISO
format, turn the parens-negative amounts (<code>($89.50)</code>) into
proper negatives, flag the disguised-null categories, and dedup the
export down to <strong>20 real transactions</strong> — six phantom
duplicates removed, 0 unparseable.
</p>
<div class="demo-frame">
<iframe
@@ -197,13 +204,17 @@
price. DataTools writes the audit by default, downloadable as a
separate CSV alongside the cleaned file.
</div>
<div class="terminal"><span class="prompt">$</span> head -5 client_jan2025_changes.csv
<div class="terminal"><span class="prompt">$</span> python -m src.cli_pipeline bank_reconciliation.csv --pipeline bank_reconciliation_pipeline.json --apply
standardize · 36 date/amount cells normalized (ISO dates, numeric amounts, parens-negatives resolved)
missing · disguised-null categories flagged (—, N/A, (blank))
dedup · 6 phantom duplicate transactions removed
rows · 26 → 20 · 0 unparseable
✓ wrote bank_reconciliation.cleaned.csv + bank_reconciliation.changes.csv (row-level audit)
<span class="prompt">$</span> head -4 bank_reconciliation.changes.csv
row,column,field_type,old,new
0,"Date ",date,"01/15/2025","2025-01-15"
0,Description,name," AMAZON.COM*4F2X9 PURCHASE","Amazon.com*4F2X9 Purchase"
0,Amount,currency,"-$129.99","-129.99"
1,Date ,date,"2025-01-15","2025-01-15"
<span class="prompt">$</span> # one row of audit per cell change. handed to the client. signed off.</div>
0,Amount,currency,"+$3,450.00","3450.00"
0,Category,category,"—","(missing)"
</div>
</section>
@@ -336,13 +347,13 @@ row,column,field_type,old,new
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for Shopify, bookkeepers, and RevOps teams.</p>
<p><strong>DataTools</strong> — local data-cleaning for bookkeepers, accounts payable, and accounts receivable teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="../shopify-pet/">For Shopify operators</a> ·
<a href="../revops/">For RevOps agencies</a><br />
<a href="../ap-1099/">For accounts payable / 1099</a> ·
<a href="../ar-aging/">For accounts receivable</a><br />
<a href="https://gumroad.com/l/datatools?from=bookkeeper">Buy on Gumroad</a> ·
<a href="mailto:hello@datatools.app">Email support</a>
</p>

2
landing/deploy.config.example.json
View File

@@ -11,7 +11,7 @@
"gumroad_listing": "https://gumroad.com/l/datatools",
"support_email": "hello@datatools.app",
"personas": ["shopify-pet", "bookkeeper", "revops"],
"personas": ["bookkeeper", "ap-1099", "ar-aging"],
"_substitutions_made": [
"{{site_origin}}/ → site_origin/",

18
landing/deploy.py
View File

@@ -7,9 +7,9 @@ to ``landing/deploy.config.json`` and filling in the real URLs:
Output:
landing/dist/index.html
landing/dist/shopify-pet/index.html
landing/dist/bookkeeper/index.html
landing/dist/revops/index.html
landing/dist/ap-1099/index.html
landing/dist/ar-aging/index.html
landing/dist/_shared/styles.css
landing/dist/robots.txt
landing/dist/sitemap.xml
@@ -50,9 +50,9 @@ EXAMPLE_PATH = LANDING / "deploy.config.example.json"
# Files to substitute and copy. Order matters only for readability.
HTML_PAGES = [
LANDING / "index.html",
LANDING / "shopify-pet" / "index.html",
LANDING / "bookkeeper" / "index.html",
LANDING / "revops" / "index.html",
LANDING / "bookkeeper" / "index.html",
LANDING / "ap-1099" / "index.html",
LANDING / "ar-aging" / "index.html",
]
SHARED = LANDING / "_shared" / "styles.css"
@@ -125,7 +125,7 @@ def _stamp_sitemap(cfg: dict) -> str:
site = cfg["site_origin"].rstrip("/")
today = date.today().isoformat()
urls = [site + "/"] + [
f"{site}/{p}/" for p in cfg.get("personas", ["shopify-pet", "bookkeeper", "revops"])
f"{site}/{p}/" for p in cfg.get("personas", ["bookkeeper", "ap-1099", "ar-aging"])
]
items = "\n".join(
f" <url><loc>{u}</loc><lastmod>{today}</lastmod></url>"
@@ -177,11 +177,11 @@ def _build_404_html(cfg: dict) -> str:
<h1>That page isn't here.</h1>
<p class="lead" style="margin: 0 auto 28px;">Pick a workflow below to land somewhere useful.</p>
<p>
<a class="btn" href="{site_origin}/shopify-pet/">For Shopify</a>
&nbsp;
<a class="btn" href="{site_origin}/bookkeeper/">For bookkeepers</a>
&nbsp;
<a class="btn" href="{site_origin}/revops/">For RevOps</a>
<a class="btn" href="{site_origin}/ap-1099/">For AP / 1099</a>
&nbsp;
<a class="btn" href="{site_origin}/ar-aging/">For AR</a>
</p>
</div>
</section>

113
landing/index.html
View File

@@ -3,13 +3,13 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools — Local CSV / Excel Cleaning for Shopify, Bookkeepers, and RevOps</title>
<meta name="description" content="One desktop tool. Three workflows. Clean Shopify customer exports, reconcile messy bank statements, or dedupe lead lists across HubSpot and LinkedIn — all locally. $49 one-time." />
<title>DataTools — Local CSV / Excel Cleaning for Bookkeepers and Accountants</title>
<meta name="description" content="One desktop tool for messy accounting exports. Reconcile bank statements, build clean 1099 vendor lists, and de-duplicate AR aging — all locally. $49 one-time." />
<link rel="canonical" href="https://datatools.app/" />
<link rel="stylesheet" href="_shared/styles.css" />
<meta property="og:title" content="DataTools — Local CSV / Excel Cleaning" />
<meta property="og:description" content="One desktop tool, three niche workflows. Runs entirely offline. $49 one-time." />
<meta property="og:title" content="DataTools — Local CSV / Excel Cleaning for Accounting" />
<meta property="og:description" content="Reconcile bank exports, prep 1099 vendor lists, clean AR aging — offline. $49 one-time." />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://datatools.app/" />
@@ -38,9 +38,9 @@
box-shadow: var(--shadow);
text-decoration: none;
}
.persona-card.shopify { --card-accent: #6ee7b7; }
.persona-card.bookkeeper{ --card-accent: #7dd3fc; }
.persona-card.revops { --card-accent: #c4b5fd; }
.persona-card.ap1099 { --card-accent: #fbbf24; }
.persona-card.ar { --card-accent: #6ee7b7; }
.persona-card .pill {
display: inline-block;
background: rgba(255,255,255,0.04);
@@ -93,70 +93,69 @@
<section class="hero">
<div class="container">
<div class="eyebrow">For Shopify operators · bookkeepers · marketing & RevOps agencies</div>
<h1>Local CSV / Excel cleaning.<br /><strong>One tool. Three workflows.</strong></h1>
<div class="eyebrow">For bookkeepers · accounts payable · accounts receivable</div>
<h1>Local CSV / Excel cleaning for accounting.<br /><strong>One tool. Three workflows.</strong></h1>
<p class="lead">
DataTools is a desktop app that fixes the data-cleaning headaches
every small business hits — duplicates Excel can't catch,
international phones it can't parse, dates and currencies in three
different formats per export. One $49 download. Works on Mac,
Windows, and Linux. <strong>Your data never leaves your
computer.</strong>
DataTools is a desktop app that fixes the export headaches that
throw off your books — the transaction your bank posted twice,
the vendor entered three ways at 1099 time, the invoice your aging
report counted twice. One $49 download. Mac, Windows, and Linux.
<strong>Your data never leaves your computer.</strong>
</p>
<div class="persona-grid">
<a class="persona-card shopify" href="shopify-pet/">
<span class="pill">🛍️ Shopify operator</span>
<h3>Customer / vendor / subscriber export cleanup</h3>
<p>
Klaviyo-import-ready customer lists in 30 seconds. Catches
cross-device duplicates, standardizes international phones
and addresses, fixes the disguised nulls that break product
feeds.
</p>
<ul class="pain">
<li>· Fix Klaviyo per-contact billing on phantom dupes</li>
<li>· Repair feeds rejected by Google Merchant / Meta</li>
<li>· Unify orders from Shopify + Etsy + Amazon + Faire</li>
<li>· Resolve VAT-MOSS country-name drift</li>
</ul>
<span class="open">Open the Shopify demo &amp; pricing</span>
</a>
<a class="persona-card bookkeeper" href="bookkeeper/">
<span class="pill">📒 Bookkeeper / accountant</span>
<h3>Bank-export reconciliation with audit trail</h3>
<span class="pill">📒 Bookkeeper</span>
<h3>Bank reconciliation with an audit trail</h3>
<p>
Catches the duplicate transaction QuickBooks imported twice
when Jan and Feb exports overlap. Standardizes dates,
amounts, and vendor casing. Hands you a row-level audit log
to share with the client.
When the Jan and Feb exports overlap, the same payment posts
twice in two formats. DataTools standardizes every date and
amount, then dedups on the real transaction so it ties out —
with a row-level audit log to hand the client.
</p>
<ul class="pain">
<li>· Catch month-overlap re-import dupes</li>
<li>· Consolidate vendors for clean 1099 reports</li>
<li>· Produce hand-off-ready audit trail</li>
<li>· Multi-currency books (EUR / GBP / BRL)</li>
<li>· Catch month-overlap re-import duplicates</li>
<li>· ISO dates, numeric amounts, parens-negatives resolved</li>
<li>· Hand-off-ready audit trail</li>
<li>· Sample: 26 rows → 20, six phantom duplicates removed</li>
</ul>
<span class="open">Open the bookkeeper demo &amp; pricing</span>
</a>
<a class="persona-card revops" href="revops/">
<span class="pill">🪢 Marketing / RevOps</span>
<h3>Lead-list dedup across HubSpot, LinkedIn, scrapes</h3>
<a class="persona-card ap1099" href="ap-1099/">
<span class="pill">🧾 Accounts payable / 1099</span>
<h3>Clean 1099 vendor list — missing EINs filled in</h3>
<p>
One canonical lead per real person — across HubSpot,
LinkedIn, Apollo, ZoomInfo, and manual scrapes.
International phones (50+ country codes), per-row country
column, fuzzy match with merge.
The same vendor entered three times, each record holding only
part of the details. DataTools consolidates each vendor to one
row and backfills the gaps from the duplicates, so the EINs you
need at filing time are recovered.
</p>
<ul class="pain">
<li>· Stop paying HubSpot tier price for cross-source dupes</li>
<li>· Protect sender reputation from invalid emails</li>
<li>· Skip the 4–8 wk GDPR review on cloud cleaners</li>
<li>· Suppression-list sync across 5+ platforms</li>
<li>· Consolidate vendor masters for 1099-NEC</li>
<li>· Recover EINs scattered across duplicate records</li>
<li>· Standardize phones, emails, and amounts</li>
<li>· Sample: 24 records → 8 vendors, 7 EINs recovered</li>
</ul>
<span class="open">Open the RevOps demo &amp; pricing</span>
<span class="open">Open the 1099 / AP demo &amp; pricing</span>
</a>
<a class="persona-card ar" href="ar-aging/">
<span class="pill">💵 Accounts receivable</span>
<h3>AR aging without the double-counted invoices</h3>
<p>
Double-entered invoices inflate your aging report and your
follow-ups. DataTools standardizes invoice dates, due dates,
and amounts, lowercases client emails, then removes the
duplicate invoice numbers so the aging is accurate.
</p>
<ul class="pain">
<li>· Remove double-entered invoices from the aging</li>
<li>· ISO dates, numeric amounts, lowercased client emails</li>
<li>· Backfill a blank status from its twin row</li>
<li>· Sample: 26 rows → 21, five duplicate invoices removed</li>
</ul>
<span class="open">Open the AR demo &amp; pricing</span>
</a>
</div>
</div>
@@ -218,14 +217,14 @@
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for Shopify, bookkeepers, and RevOps teams.</p>
<p><strong>DataTools</strong> — local data-cleaning for bookkeepers, accounts payable, and accounts receivable teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="shopify-pet/">For Shopify operators</a> ·
<a href="bookkeeper/">For bookkeepers</a> ·
<a href="revops/">For RevOps agencies</a><br />
<a href="ap-1099/">For accounts payable / 1099</a> ·
<a href="ar-aging/">For accounts receivable</a><br />
<a href="mailto:hello@datatools.app">Email support</a>
</p>
</div>

352
landing/revops/index.html
View File

@@ -1,352 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools for RevOps — Dedupe Lead Lists Across HubSpot, LinkedIn, and Manual Scrapes · $49</title>
<meta name="description" content="One tool to dedupe lead lists across HubSpot, LinkedIn, and manual scrapes. International phones (50+ country codes), per-row country normalization, fuzzy match across vendors, fully offline. $49 one-time." />
<meta name="keywords" content="dedupe lead list, hubspot deduplicate, linkedin lead cleanup, marketing data cleaning, revops csv tool, multi-vendor lead unification, international phone normalization" />
<link rel="canonical" href="https://datatools.app/revops/" />
<link rel="stylesheet" href="../_shared/styles.css" />
<!-- Persona accent: RevOps → vivid violet -->
<style>
:root {
--accent: #c4b5fd;
--accent-ink: #2e1065;
}
</style>
<meta property="og:title" content="DataTools for RevOps — Dedupe Lead Lists Across HubSpot, LinkedIn, and Manual Scrapes" />
<meta property="og:description" content="International phones, country normalization, fuzzy dedup with merge — one tool, no upload. $49 one-time." />
<meta property="og:type" content="product" />
<meta property="og:url" content="https://datatools.app/revops/" />
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "DataTools for RevOps",
"operatingSystem": "Windows, macOS, Linux",
"applicationCategory": "BusinessApplication",
"offers": {
"@type": "Offer",
"price": "49",
"priceCurrency": "USD"
},
"description": "Dedupe and unify lead lists across CRM, scraping, and manual sources. International phone normalization, per-row country, fuzzy match with merge. Six-tool data-cleaning bundle for RevOps and marketing agencies.",
"softwareVersion": "1.0"
}
</script>
</head>
<body>
<div class="buybar">
<div class="buybar-inner">
<div class="brand"><span class="brand-mark">●</span> DataTools <span class="muted">/ for RevOps</span></div>
<div>
<span class="price-tag">$49 — one-time, no subscription</span>
<a class="btn" href="https://gumroad.com/l/datatools?from=revops" rel="noopener">Get DataTools →</a>
</div>
</div>
</div>
<section class="hero">
<div class="container">
<div class="eyebrow">For RevOps · marketing ops · agency lead-gen · audience-builders</div>
<h1>Dedupe lead lists across HubSpot, LinkedIn,<br /><strong>and manual scrapes — locally.</strong></h1>
<p class="lead">
The same prospect shows up as <code>alice@acme.com</code> in HubSpot,
<code>Alice.Johnson@acme.com</code> in LinkedIn Sales Navigator, and
<code>alice@acme.com</code> again from your VA's manual scrape. Their
phone is <code>(415) 555-1234</code> in one source and
<code>4155551234</code> in another. DataTools fuzzy-matches across
sources, normalizes phones to E.164 with per-row country awareness,
and produces one canonical lead per real person — without uploading
a single contact to a third-party tool.
</p>
<div class="cta-row">
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=revops" rel="noopener">Get DataTools — $49 →</a>
<a class="btn btn-ghost btn-large" href="#demo">Try the live demo ↓</a>
<span class="price-note">One-time payment · cross-platform · runs offline</span>
</div>
<div class="stats">
<div class="stat"><div class="num">50+</div><div class="label">country codes</div></div>
<div class="stat"><div class="num">3</div><div class="label">CRM sources unified</div></div>
<div class="stat"><div class="num">0</div><div class="label">cloud uploads ever</div></div>
</div>
</div>
</section>
<!-- ============= Pain points ============= -->
<section>
<div class="container">
<div class="eyebrow">If your last campaign launch was held up by data hygiene</div>
<h2>Five pains DataTools fixes before you import to HubSpot</h2>
<div class="grid">
<div class="card">
<span class="icon">💸</span>
<h3>HubSpot / Marketo / Iterable bills you for every duplicate contact</h3>
<p>10 k contacts → enterprise tier at $4–8 k/mo. 18 % cross-source duplicate rate from Apollo + ZoomInfo + LinkedIn means you're at 8.2 k unique people but paying for 10 k. Every month. Forever.</p>
<p class="muted"><strong>What it costs:</strong> $200–$800 per 1 k duplicate contacts — recurring, every month.</p>
</div>
<div class="card">
<span class="icon">🚫</span>
<h3>Sender reputation tanks when you mail to invalid or duplicate addresses</h3>
<p>One bad sending session — to addresses your team scraped or imported without hygiene — and your domain reputation takes weeks to recover. Your good campaigns sit in spam folders during the recovery.</p>
<p class="muted"><strong>What it costs:</strong> catastrophic — entire email programme degraded for 2–6 weeks.</p>
</div>
<div class="card">
<span class="icon">⚖️</span>
<h3>GDPR makes uploading to a cloud cleaner a legal-review marathon</h3>
<p>Every cloud-based lead-cleaner needs you to upload your prospect list. Your legal team needs 4–8 weeks to bless that. DataTools is desktop-only — no upload, no DPA, no review, no delay.</p>
<p class="muted"><strong>What it costs:</strong> 4–8 weeks of legal-review delay per tool, every time.</p>
</div>
<div class="card">
<span class="icon">🪢</span>
<h3>Apollo + ZoomInfo + LinkedIn + manual scrapes all use different schemas</h3>
<p>Each export has its own column names, scoring scale, country format. Unifying them by hand for one campaign costs 1–3 days. Doing it for every campaign is unsustainable.</p>
<p class="muted"><strong>What it costs:</strong> 1–3 days per campaign of manual unification + judgement calls that drift across team members.</p>
</div>
<div class="card">
<span class="icon">🛡️</span>
<h3>Suppression lists across 5+ marketing platforms get out of sync</h3>
<p>Each platform has its own suppression format. Out-of-sync lists let opted-out contacts slip through, triggering CAN-SPAM / GDPR exposure and the kind of "we got a complaint" email no one wants.</p>
<p class="muted"><strong>What it costs:</strong> compliance risk + churn-back cost + stakeholder trust.</p>
</div>
<div class="card">
<span class="icon">📞</span>
<h3>International dialer fails because phone formats vary</h3>
<p>Calling list to 15 countries with mixed formats means dialler rejects 8–15 % of numbers, your reps spend the day on "number invalid" tones instead of conversations.</p>
<p class="muted"><strong>What it costs:</strong> rep productivity × failure rate × team size.</p>
</div>
</div>
</div>
</section>
<section id="demo">
<div class="container">
<div class="eyebrow">Live demo · runs in your browser</div>
<h2>Try it on a real-looking 3-vendor lead list</h2>
<p>
The demo below loads a 25-row lead worksheet combining HubSpot,
LinkedIn Sales Navigator, and manual scraping — with the same prospect
appearing in two or three sources, country names spelled three
different ways (<code>USA</code>, <code>US</code>, <code>United
States</code>), and 13 different international phone formats. Click
<strong>Run pipeline</strong> and watch the 5-step pipeline (text
clean → format → missing → column map → dedup) collapse 25 rows to 19
with a single canonical record per prospect.
</p>
<div class="demo-frame">
<iframe
src="https://demo.datatools.app/?p=revops"
loading="lazy"
title="DataTools live demo — RevOps"
sandbox="allow-scripts allow-same-origin allow-downloads allow-forms"></iframe>
<div class="demo-caption">
Demo runs on free hosting. Capped at 100 input rows · output
watermarked. The paid product has no caps and runs entirely offline.
</div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">Built for the agency RevOps day</div>
<h2>Three workflows you do every campaign</h2>
<div class="grid">
<div class="card">
<span class="icon">🪢</span>
<h3>Email-list dedup across lead sources</h3>
<p>HubSpot exports + LinkedIn Sales Navigator + the VA's spreadsheet, all merged. Fuzzy match across email + phone + name catches the cross-source duplicates that broke your last campaign send.</p>
</div>
<div class="card">
<span class="icon">🌍</span>
<h3>Multi-platform audience reconciliation</h3>
<p>Build one canonical audience from Meta, Google Ads, LinkedIn, and your CRM. Each platform exports a different shape; Map Columns aligns them all, dedup merges the survivors with their most-complete fields.</p>
</div>
<div class="card">
<span class="icon">🛡️</span>
<h3>Suppression-list management</h3>
<p>Suppression lists need to dedupe across email + phone + first-party identifiers. Add a row, dedupe, ship the canonical CSV to every platform — without uploading the suppression list to any of them.</p>
</div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">If your campaigns target outside the US — almost everyone's do</div>
<h2>50+ country codes. Per-row country awareness.</h2>
<p>
Your HubSpot list has <code>(415) 555-1234</code>. Your scraped
list from the same prospect has <code>+1 415 555 1234</code>. Your
Italian prospect entered <code>+39 06 6982</code>. Your Brazilian
lead has <code>11 3071 0000</code>. Each comes from a row tagged
with its country — DataTools reads that column per row and parses
every phone correctly to E.164.
</p>
<ul class="bullets">
<li><strong>Per-row country column</strong> drives the parser — no global default that bucks UK numbers as malformed US.</li>
<li><strong>Country-name normalization</strong>: <code>USA</code> / <code>US</code> / <code>United States</code> all resolve to the same ISO-2 code.</li>
<li><strong>50+ country support</strong> via Google's libphonenumber, including KR, CN, IN, MX, BR, IL, TR, PL, DK, SE.</li>
<li><strong>Schema enforcement</strong> via Map Columns: project to your CRM's required shape, coerce score columns to integers, reorder fields to match the import contract.</li>
</ul>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">For platforms that charge per contact</div>
<h2>Every duplicate you don't catch costs you for the life of the contract.</h2>
<p>
HubSpot prices on contacts. Klaviyo prices on contacts. Marketo,
Iterable, ActiveCampaign — all priced on contacts. Every duplicate
you don't catch is a recurring tax on your campaign. DataTools
catches them once, before import, with a fuzzy matcher that's
tuned to the cross-source noise you actually see.
</p>
<div class="callout">
<strong>Real numbers from the demo:</strong> 25 input rows from
three sources collapse to 19 — that's 6 duplicates the cross-source
noise was hiding. On a 50,000-row campaign list, that ratio
typically saves 12,000+ contacts a month, every month.
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">The thing every cloud cleaner can't say</div>
<h2>Your prospects' contact info never leaves your computer.</h2>
<p>
Cloud lead-cleaning tools require you to upload your audience.
That audience is your single most valuable agency asset — and once
it's on someone else's server, your client's privacy story is
no longer in your hands. DataTools is a desktop app. There is no
upload step.
</p>
<div class="terminal"><span class="prompt">$</span> python -m src.cli_pipeline campaign_q1.csv --pipeline revops_pipeline.json --apply
Reading campaign_q1.csv...
53,802 rows, 14 columns
Executing pipeline:
<span class="ok">✓</span> text_clean (160 ms) {cells_changed: 8,205}
<span class="ok">✓</span> format_standardize (1.4 s) {cells_changed: 41,889 — 50 country codes}
<span class="ok">✓</span> missing (140 ms) {sentinels_standardized: 6,710}
<span class="ok">✓</span> column_map (220 ms) {columns_renamed: 4, columns_added: 1}
<span class="ok">✓</span> dedup (4.8 s) {duplicates_removed: 12,344, merged: 12,344}
Initial rows: 53,802 → Final rows: 41,458
Total elapsed: 6.7 s
<span class="prompt">$</span> # 12,344 fewer contacts to pay for. for $49.</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">In the bundle</div>
<h2>Six tools. One pipeline. One $49 download.</h2>
<div class="grid">
<div class="card"><h3>1 · Find Duplicates</h3><p>Fuzzy match across email + phone + name + company; merge survivors with most-complete fields.</p></div>
<div class="card"><h3>2 · Clean Text</h3><p>Smart quotes from copy-paste, NBSP from spreadsheet exports, BOM from Excel.</p></div>
<div class="card"><h3>3 · Standardize Formats</h3><p>E.164 phones with per-row country, canonical emails, name casing, ISO dates.</p></div>
<div class="card"><h3>4 · Fix Missing Values</h3><p>Detect <code>TBD</code>, <code>(unknown)</code>, <code>—</code> across vendor exports.</p></div>
<div class="card"><h3>5 · Map Columns</h3><p>Project to your CRM's required schema, coerce score to integer, reorder for import.</p></div>
<div class="card"><h3>6 · Automated Workflows</h3><p>Save the cleanup as JSON. Drop next campaign's combined export on it. Same dedup, automated.</p></div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="eyebrow">Pricing — pay once, own it</div>
<h2>$49. No subscription. No per-campaign fee.</h2>
<div class="pricing">
<div class="card featured">
<div class="row"><div class="price">$49</div><div class="price-suffix">one-time</div></div>
<h3>DataTools for RevOps</h3>
<ul>
<li>All 6 tools, full pipeline</li>
<li>Mac · Windows · Linux installers</li>
<li>Code-signed (no Gatekeeper warnings)</li>
<li>Free updates for the v1.x line</li>
<li>Bonus: 3-source unification pipeline preset</li>
<li><strong>Use on any number of clients</strong> — no seat limits</li>
</ul>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=revops" rel="noopener">Buy on Gumroad →</a>
</div>
<div class="card">
<div class="row"><div class="price">$149</div><div class="price-suffix">one-time</div></div>
<h3>Full DataTools Suite</h3>
<p class="muted">Available when 3+ bundles ship. Includes everything in the RevOps pack plus the Shopify and Bookkeeper bundles. Save $48.</p>
<a class="btn btn-ghost btn-large" href="#" aria-disabled="true">Coming when ready</a>
</div>
</div>
</div>
</section>
<section>
<div class="container">
<h2>Questions</h2>
<details class="faq">
<summary>Does this replace HubSpot's deduplication?</summary>
<p>No — it cleans data <em>before</em> import to HubSpot (or LinkedIn, Marketo, Klaviyo, etc.). HubSpot's dedup runs on already-imported contacts; DataTools catches duplicates that haven't yet cost you a contract slot.</p>
</details>
<details class="faq">
<summary>Does it handle international phones correctly?</summary>
<p>Yes — via Google's libphonenumber, with 50+ country codes. The killer feature is per-row country: point a column at it (any column with values like <code>US</code>, <code>USA</code>, <code>United States</code>, <code>+1</code>, <code>JP</code>, <code>Japan</code>) and DataTools parses each row in its own region. No more UK numbers bucketed as malformed US.</p>
</details>
<details class="faq">
<summary>Can I use it on multiple clients without paying again?</summary>
<p>Yes. The licence is per-operator, not per-client. Run it on every agency client's lead list for the same $49.</p>
</details>
<details class="faq">
<summary>How does fuzzy match work across columns?</summary>
<p>Out of the box, the dedup engine builds default strategies based on column names — typically email + phone with exact match, name with Jaro-Winkler at 85%. You can override via JSON: pick which columns to match on, which algorithm, and what threshold. Strategies survive in the saved pipeline so next campaign uses the same rules.</p>
</details>
<details class="faq">
<summary>What's the audit trail look like?</summary>
<p>A row-by-row CSV: every modified cell with its original value, new value, and which rule fired. A separate JSON file describes the pipeline that produced it. Together they reproduce the cleanup deterministically — your client can verify it on their machine.</p>
</details>
<details class="faq">
<summary>What's your refund policy?</summary>
<p>Try the live demo above on the sample dataset before you buy. If DataTools doesn't fit your workflow within 14 days, email for a refund — no questions asked.</p>
</details>
</div>
</section>
<section>
<div class="container" style="text-align: center;">
<h2>Stop paying twice for the same contact.</h2>
<p class="lead" style="margin: 0 auto 28px;">One $49 download. Catches the cross-source duplicates HubSpot and LinkedIn can't see, normalizes phones for 50+ countries, and saves a pipeline you can re-run on next campaign's combined list.</p>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=revops" rel="noopener">Get DataTools — $49 →</a>
</div>
</section>
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for Shopify, bookkeepers, and RevOps teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="../shopify-pet/">For Shopify operators</a> ·
<a href="../bookkeeper/">For bookkeepers</a><br />
<a href="https://gumroad.com/l/datatools?from=revops">Buy on Gumroad</a> ·
<a href="mailto:hello@datatools.app">Email support</a>
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<!DOCTYPE html>
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<head>
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<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>DataTools for Shopify — Clean Customer & Product Exports Locally · $49</title>
<meta name="description" content="Clean Shopify customer, product, and subscriber exports — locally. Klaviyo-import-ready in 30 seconds. Catches duplicates Excel misses. Your data never leaves your computer. $49 one-time." />
<meta name="keywords" content="shopify customer cleanup, shopify csv cleaner, shopify product feed cleaner, klaviyo deduplicate, shopify customer dedup tool, shopify pet supplies" />
<link rel="canonical" href="https://datatools.app/shopify/" />
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<meta property="og:description" content="Klaviyo-import-ready in 30 seconds. Local. No upload. $49 one-time." />
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<meta property="og:url" content="https://datatools.app/shopify/" />
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"name": "DataTools for Shopify",
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"offers": {
"@type": "Offer",
"price": "49",
"priceCurrency": "USD"
},
"description": "Clean Shopify customer, product, and subscriber CSV exports locally. Six-tool data-cleaning bundle: dedupe, text-clean, format-standardize, missing-value handle, column-map, pipeline.",
"softwareVersion": "1.0"
}
</script>
</head>
<body>
<!-- ============= Sticky buy bar ============= -->
<div class="buybar">
<div class="buybar-inner">
<div class="brand"><span class="brand-mark">●</span> DataTools <span class="muted">/ for Shopify</span></div>
<div>
<span class="price-tag">$49 — one-time, no subscription</span>
<a class="btn" href="https://gumroad.com/l/datatools?from=shopify-pet" rel="noopener">Get DataTools →</a>
</div>
</div>
</div>
<!-- ============= Hero ============= -->
<section class="hero">
<div class="container">
<div class="eyebrow">For Shopify operators · pet supplies · subscription stores · DTC</div>
<h1>Klaviyo-import-ready customer lists.<br /><strong>In 30 seconds. Locally.</strong></h1>
<p class="lead">
Your Shopify customer export is a mess of formatting drift, disguised
duplicates, and inconsistent phone numbers. DataTools fixes all of it
in one pass — fuzzy-dedupes the same customer Klaviyo would charge
you for twice, standardises phones across your international
subscribers, and hands you a cleaned CSV. <strong>Your data never
leaves your computer.</strong>
</p>
<div class="cta-row">
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=shopify-pet" rel="noopener">Get DataTools — $49 →</a>
<a class="btn btn-ghost btn-large" href="#demo">Try the live demo ↓</a>
<span class="price-note">One-time payment · cross-platform · runs offline</span>
</div>
<div class="stats">
<div class="stat"><div class="num">6</div><div class="label">tools, one bundle</div></div>
<div class="stat"><div class="num">1 GB</div><div class="label">customer file in 2.5 min</div></div>
<div class="stat"><div class="num">0</div><div class="label">cloud uploads ever</div></div>
</div>
</div>
</section>
<!-- ============= Pain points ============= -->
<section>
<div class="container">
<div class="eyebrow">If any of these sound like your Tuesday</div>
<h2>Five pains DataTools fixes in one pass</h2>
<div class="grid">
<div class="card">
<span class="icon">💸</span>
<h3>Klaviyo / Mailchimp / Omnisend bills you for every duplicate</h3>
<p>Same customer signs up twice — once with a typo, once with a plus-tag, once on mobile. Your subscriber list has 10–18 % duplicate rate and you're paying for every one of them, every month, forever.</p>
<p class="muted"><strong>What it costs:</strong> $30–$300/mo per percent of dupes on a 50 k-list — recurring.</p>
</div>
<div class="card">
<span class="icon">📵</span>
<h3>Your product feed got rejected by Google Merchant Center</h3>
<p>Smart quotes from a copy-paste in product titles. NBSP in SKU. Inconsistent attribute casing. Feed bounces, the launch sits for 24–72 hours while you try to find the bad row in a 12,000-line CSV.</p>
<p class="muted"><strong>What it costs:</strong> 1–3 days of delayed campaign × the campaign value.</p>
</div>
<div class="card">
<span class="icon">🪢</span>
<h3>Orders from Shopify + Etsy + Amazon + Faire don't speak the same language</h3>
<p>Each platform's export uses different column names for "customer email" / "ship country" / "order total." Merging takes hours of manual rename and copy-paste before the analysis can even begin.</p>
<p class="muted"><strong>What it costs:</strong> 4–8 hours per month manually merging exports.</p>
</div>
<div class="card">
<span class="icon">🔁</span>
<h3>Subscription churn looks higher than it is</h3>
<p>Pet-box subscribers cancel, then re-sub three months later under a different email or device. Your cohort report says churn is 20 % when it's actually 12 % — and you're over-paying for acquisition because LTV is mis-calculated.</p>
<p class="muted"><strong>What it costs:</strong> wrong CAC ceiling for the next year of paid ads.</p>
</div>
<div class="card">
<span class="icon">🌍</span>
<h3>VAT MOSS / EU tax breaks because country is spelled three ways</h3>
<p>Your UK customers are tagged <code>UK</code>, <code>U.K.</code>, and <code>United Kingdom</code> — all in one export. The VAT report aggregates them as three different markets. Compliance friction every quarter.</p>
<p class="muted"><strong>What it costs:</strong> compliance risk + repeated manual normalization.</p>
</div>
<div class="card">
<span class="icon">🔒</span>
<h3>Cloud cleaners want you to upload your customer list</h3>
<p>Your customer list is your single most valuable business asset. Uploading it to a SaaS to clean it is the privacy story you do not want. DataTools is desktop-only — your list never leaves your computer.</p>
<p class="muted"><strong>What it costs:</strong> nothing — and that's the point.</p>
</div>
</div>
</div>
</section>
<!-- ============= Live demo ============= -->
<section id="demo">
<div class="container">
<div class="eyebrow">Live demo · runs in your browser</div>
<h2>Try it on a real-looking Shopify customer export</h2>
<p>
The demo below loads a sample 15-row Shopify customer file with
pollution we've seen in actual stores: smart quotes from copy-paste,
duplicates with email-case drift, international phones from the UK,
Spain, Germany, Australia, and Japan, and the usual mess of
<code>N/A</code> / <code>(blank)</code> / <code>?</code> sentinels.
Click <strong>Run pipeline</strong> and watch every column get
cleaned in under a second.
</p>
<div class="demo-frame">
<iframe
src="https://demo.datatools.app/?p=shopify-pet"
loading="lazy"
title="DataTools live demo — Shopify pet supplies"
sandbox="allow-scripts allow-same-origin allow-downloads allow-forms"></iframe>
<div class="demo-caption">
Demo runs on free hosting (Streamlit Community Cloud). Capped at
100 input rows · output watermarked with one trailing row. The
paid product has no caps and runs entirely offline.
</div>
</div>
</div>
</section>
<!-- ============= Built for Shopify ============= -->
<section>
<div class="container">
<div class="eyebrow">Built for the Shopify operator</div>
<h2>Five workflows you do every week</h2>
<div class="grid">
<div class="card">
<span class="icon">🧹</span>
<h3>Customer-list cleanup</h3>
<p>Catches the same customer who shows up as <code>john@gmail.com</code>, <code>John@Gmail.com</code>, and <code>j.ohn@gmail.com</code>. Fuzzy match merges the spellings, exact match catches the obvious ones.</p>
</div>
<div class="card">
<span class="icon">📦</span>
<h3>Product catalogue dedup</h3>
<p>SKU whitespace, near-identical product names, copy-paste smart quotes in titles — gone. Audit log shows every change.</p>
</div>
<div class="card">
<span class="icon">🛒</span>
<h3>Abandoned-cart hygiene</h3>
<p>Before re-engagement: dedupe across email + phone, drop sentinels-as-missing, format dates so your sequence triggers fire correctly.</p>
</div>
<div class="card">
<span class="icon">📥</span>
<h3>Subscriber-list import to Klaviyo</h3>
<p>Klaviyo charges per contact. Every duplicate you don't catch costs you for the life of the subscription. Catch them once, pay once.</p>
</div>
<div class="card">
<span class="icon">🔗</span>
<h3>Multi-channel order consolidation</h3>
<p>Orders from Shopify + Etsy + a wholesale spreadsheet, each with a different column for "customer email." Map Columns aligns them; dedup merges across channels.</p>
</div>
<div class="card">
<span class="icon">⚙️</span>
<h3>Repeatable pipeline</h3>
<p>Save the cleanup as a JSON file. Drop next week's export on it. Same cleanup, zero re-configuration. Automatable via the CLI.</p>
</div>
</div>
</div>
</section>
<!-- ============= Privacy moat ============= -->
<section>
<div class="container">
<div class="eyebrow">The thing every cloud cleaner can't say</div>
<h2>Your customer list never leaves your computer.</h2>
<p>
DataTools is a desktop app. There's no upload step, no SaaS account,
no subscription, no "trust our security policy." The first thing you
can do after install is open your browser's network tab, run the
cleaner on your real customer file, and verify zero outbound
requests.
</p>
<div class="callout">
<strong>Why it matters for Shopify:</strong> your customer list is
your single most valuable business asset. Cloud cleaners require
you to upload it. We don't.
</div>
<div class="terminal"><span class="prompt">$</span> python -m src.cli_pipeline customers.csv --apply
Reading customers.csv...
47,832 rows, 14 columns
Executing pipeline:
<span class="ok">✓</span> text_clean (140 ms) {cells_changed: 12,408}
<span class="ok">✓</span> format_standardize (810 ms) {cells_changed: 31,202}
<span class="ok">✓</span> missing (95 ms) {sentinels_standardized: 8,129}
<span class="ok">✓</span> dedup (3.1 s) {duplicates_removed: 2,347}
Initial rows: 47,832 → Final rows: 45,485
Total elapsed: 4.2 s
<span class="prompt">$</span> # zero network calls. zero. promise.</div>
</div>
</section>
<!-- ============= Audit moat ============= -->
<section>
<div class="container">
<div class="eyebrow">For when your client asks "what changed?"</div>
<h2>Every change auditable. Every cell logged.</h2>
<p>
Every modification is recorded with the original value, the new
value, and which rule fired. Hand the audit CSV to your accountant,
your marketing manager, or your boss along with the cleaned file.
No <em>"I trust the AI"</em> hand-waving — they see exactly what
happened.
</p>
<div class="callout">
<strong>Real example:</strong> the demo above standardized 27
cells across 15 customers. The audit log lists each one — row,
column, before, after, which standardizer fired. The dedup audit
lists every duplicate group with the survivor and its losers.
</div>
</div>
</section>
<!-- ============= International ============= -->
<section>
<div class="container">
<div class="eyebrow">If you sell internationally — most pet brands do</div>
<h2>Phones, addresses, and currencies from anywhere on Earth.</h2>
<p>
Your subscriber from London entered her phone as <code>020 7946
0958</code>. Your Tokyo customer entered <code>03-3210-7000</code>.
Your German wholesale buyer wrote <code>€2.410,75</code>. Excel
thinks all of them are mistakes. DataTools knows what country each
row is from (per-row country column) and parses every one correctly
to E.164 phones, ISO dates, and numeric amounts.
</p>
<ul class="bullets">
<li><strong>50+ country codes</strong> via Google's libphonenumber.</li>
<li><strong>Currency auto-detect</strong> for $ / £ / € / ¥ / R$ / kr / zł — including the EU comma-decimal that breaks Excel.</li>
<li><strong>Address shape detection</strong> for US, UK, Canada, Germany, Australia.</li>
<li><strong>Locale-aware month names</strong> in English, French, German.</li>
</ul>
</div>
</section>
<!-- ============= What you get ============= -->
<section>
<div class="container">
<div class="eyebrow">In the bundle</div>
<h2>Six tools. One pipeline. One $49 download.</h2>
<div class="grid">
<div class="card"><h3>1 · Find Duplicates</h3><p>Fuzzy match (Jaro-Winkler), 5 normalizers, survivor rules, interactive review.</p></div>
<div class="card"><h3>2 · Clean Text</h3><p>Whitespace, smart chars, NBSP, BOM, line endings, case ops.</p></div>
<div class="card"><h3>3 · Standardize Formats</h3><p>Dates, phones, emails, addresses, names, currencies, booleans.</p></div>
<div class="card"><h3>4 · Fix Missing Values</h3><p>Disguised-null detection, profile, mean/median/mode/ffill, drop strategies.</p></div>
<div class="card"><h3>5 · Map Columns</h3><p>Fuzzy auto-rename, target schema, type coercion, required-field defaults.</p></div>
<div class="card"><h3>6 · Automated Workflows</h3><p>Chain tools in recommended order, save/load JSON, automate weekly cleanups.</p></div>
</div>
</div>
</section>
<!-- ============= Pricing ============= -->
<section>
<div class="container">
<div class="eyebrow">Pricing — pay once, own it</div>
<h2>$49. No subscription. No ceiling on rows or files.</h2>
<div class="pricing">
<div class="card featured">
<div class="row"><div class="price">$49</div><div class="price-suffix">one-time</div></div>
<h3>DataTools for Shopify</h3>
<ul>
<li>All 6 tools, full pipeline</li>
<li>Mac · Windows · Linux installers</li>
<li>Code-signed (no Gatekeeper warnings)</li>
<li>Free updates for the v1.x line</li>
<li>Bonus: 3 ready-made Shopify pipelines</li>
</ul>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=shopify-pet" rel="noopener">Buy on Gumroad →</a>
</div>
<div class="card">
<div class="row"><div class="price">$149</div><div class="price-suffix">one-time</div></div>
<h3>Full DataTools Suite</h3>
<p class="muted">Available when 3+ bundles ship. Includes everything in the Shopify pack plus the Bookkeeper and RevOps bundles. Save $48.</p>
<a class="btn btn-ghost btn-large" href="#" aria-disabled="true">Coming when ready</a>
</div>
</div>
</div>
</section>
<!-- ============= FAQ ============= -->
<section>
<div class="container">
<h2>Questions</h2>
<details class="faq">
<summary>Does this work with Shopify Plus?</summary>
<p>Yes — the input is just CSV / Excel from any source. Your Shopify Plus exports work the same as the standard plan, the same as a Shopify-to-CSV pipeline you've stitched together yourself. The cleaner doesn't care.</p>
</details>
<details class="faq">
<summary>How does this compare to Excel's "Remove Duplicates"?</summary>
<p>Excel does <em>exact</em> deduplication. <code>John@Gmail.com</code> and <code>john@gmail.com</code> are different customers to Excel. DataTools fuzzy-matches across case, whitespace, formatting, and even close-but-not-identical strings. The demo above merges 4 customer pairs Excel would leave duplicated.</p>
</details>
<details class="faq">
<summary>How big a file can it handle?</summary>
<p>1 GB CSV with international phones + addresses processes in about 2.5 minutes on a typical workstation. Streaming mode keeps memory bounded regardless of input size — we tested it on 26 million rows.</p>
</details>
<details class="faq">
<summary>Do I need to know Python to use it?</summary>
<p>No. The GUI is a browser interface that opens automatically when you double-click the app. It loads your file, you click Run, you download the cleaned file. The CLI is there for power users who want to script weekly cleanups.</p>
</details>
<details class="faq">
<summary>What about my privacy?</summary>
<p>Your customer list never leaves your computer. There is no cloud component, no telemetry, no "anonymous usage stats." When the app is running you can confirm zero outbound network requests in your browser's developer tools.</p>
</details>
<details class="faq">
<summary>What's your refund policy?</summary>
<p>Try the live demo above on the sample dataset before you buy. If you still find DataTools doesn't fit your workflow within 14 days, email for a refund — no questions asked.</p>
</details>
<details class="faq">
<summary>Will there be updates?</summary>
<p>Yes. The v1.x line is included free for everyone who buys DataTools today. We ship a patch every 30 days adding country support, edge-case fixes, and small features.</p>
</details>
</div>
</section>
<!-- ============= Final CTA ============= -->
<section>
<div class="container" style="text-align: center;">
<h2>Stop deduplicating customers by hand.</h2>
<p class="lead" style="margin: 0 auto 28px;">One $49 download. Mac, Windows, or Linux. Runs offline. Catches the duplicates Excel misses, standardizes the phones from your international customers, and saves a pipeline you can re-run on next week's export.</p>
<a class="btn btn-large" href="https://gumroad.com/l/datatools?from=shopify-pet" rel="noopener">Get DataTools — $49 →</a>
</div>
</section>
<!-- ============= Footer ============= -->
<footer>
<div class="container">
<div>
<p><strong>DataTools</strong> — local data-cleaning for Shopify, bookkeepers, and RevOps teams.</p>
<p class="muted">© 2026 · Built solo · Shipped from a small office.</p>
</div>
<div>
<p>
<a href="../bookkeeper/">For bookkeepers</a> ·
<a href="../revops/">For RevOps agencies</a><br />
<a href="https://gumroad.com/l/datatools?from=shopify-pet">Buy on Gumroad</a> ·
<a href="mailto:hello@datatools.app">Email support</a>
</p>
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