How to Define Custom Columns
and Extract Data from Any App Screenshot
Take a screenshot of a Salesforce deal card showing a $47,200 opportunity closing March 15. Take another of a QuickBooks invoice tab listing the same deal. Take a third from your company's internal order-tracking dashboard built in 2011. Three screenshots, three completely different visual layouts, one shared truth: the same customer, the same amount, the same close date. If you define your extraction columns once — "Customer", "Deal Amount", "Close Date" — those three words find their values in all three screenshots, without knowing in advance where any of them sits on any given page.
The Same Data, Three Radically Different Screenshots
The scenario above is not hypothetical. Anyone who works across multiple software platforms knows the routine: Salesforce stores the opportunity data. QuickBooks stores the invoice data. The operations dashboard stores the fulfillment status. Each system displays overlapping information in its own visual language — cards, tables, forms, status panels — and pulling that data into a single spreadsheet requires switching between windows and retyping the same fields from three different interfaces.
The standard answer to this problem — "use an API" or "export to CSV" — assumes every system has those capabilities. The internal dashboard built in 2011 definitely doesn't. The mobile banking app showing a transaction detail you need for reconciliation has no export button. The SaaS analytics tool your team uses only exports full reports, not the three specific KPI values you track weekly.
Screenshots are the lowest common denominator across all software — every app, every device, every era of software can produce one. The question is not whether you can capture the data (you already did, by taking the screenshot). The question is whether you can extract specific fields from that capture without treating every new app interface as a separate technical problem.
Template-based extraction tools — the kind that ask you to draw a rectangle around the invoice date field on a sample document, then use those pixel coordinates on every subsequent document — never work for screenshots. A Salesforce deal card puts the dollar amount in a large hero number at the top left; a QuickBooks invoice puts it in a "Balance Due" cell at the bottom right of a table; an internal dashboard puts it in a colored card midway down a grid of metrics. Three UIs, three coordinate systems. A template that works on the first screenshot breaks on the second by design — the coordinates mean nothing outside the app they were measured in.
Screenshots from different applications share no coordinate system. A field extraction method built on pixel positions is building on sand. The method has to understand what the data means, not where it happens to sit.
This is the shift that makes custom column extraction worth understanding for anyone who works with screenshots. It's not a faster version of the rectangle-drawing approach — it's a fundamentally different principle.
How Custom Column Extraction Finds Values Without Knowing Where They Are
Custom column extraction works on a single principle: you describe what you want to find, and the AI searches the entire page for values that match that description — not by scanning for matching label text, but by understanding what the label means and identifying values that fit its semantic role.
When you type "Deal Amount" as a column name, you're not telling the AI to look for the exact text "Deal Amount" on the page. You're telling it: "find a monetary value that represents the total value of a business transaction or opportunity." The AI reads the entire screenshot — every label, every number, every piece of context text — and locates the value that fills that semantic role. In the Salesforce screenshot, that might be "$47,200" displayed prominently at the top of an opportunity card. In the QuickBooks screenshot, it might be "$47,200" sitting in the "Balance Due" row of an invoice table. In the internal dashboard, it might be "47,200" shown inside a colored metric card with no currency symbol at all.
The same column name works across all three because the AI is solving a semantic problem, not a spatial one. It's asking "which number on this page functions as the deal amount?" — not "what text is at coordinates (340, 128)?" The distinction is the difference between understanding a document and photographing it.
This is why the approach is sometimes called column-name extraction: the names you type into the column headers of your output table double as the extraction instructions. There's no separate step where you configure rules, no training phase where you annotate sample documents, no template library to maintain. The same column names that appear in your final Excel file are the ones the AI used to find the data.
Template OCR answers "what text is at position (x, y)?" Custom column extraction answers "where on this page is the value that means 'Deal Amount'?" The first question breaks the moment you switch apps. The second doesn't depend on the app at all.
For a broader look at how screenshot extraction differs from traditional OCR, see our complete guide to extracting data from screenshots.
Step by Step: Define Your Columns Once, Extract from Any Screenshot
The workflow has three steps, and step one is the only one you do more than once. That asymmetry is the whole point: you invest the thinking upfront, and every subsequent screenshot runs through the same definition with zero reconfiguration.
Step 1: Name the fields you need. This is the only decision you make. Open the extraction tool and type the column names that correspond to the data points you want — exactly as you want them to appear in your output spreadsheet. For a cross-app deal tracking workflow, you might enter:
Customer | Deal Amount | Close Date | Stage | Owner
For a financial reconciliation workflow pulling from banking app screenshots, payment confirmations, and accounting system dashboards, you might enter:
Transaction Date | Amount | Counterparty | Reference | Source App
These column names are your extraction specification. The AI reads them as instructions — "find values matching this description" — not as literal search strings. The label "Customer" will match "Client", "Account Name", "Bill To", or any other semantically equivalent field across different app UIs. For details on making your column names as precise as possible, jump to the naming strategy section below.
Step 2: Upload your screenshots. Drag in the Salesforce deal card, the QuickBooks invoice tab, and the internal dashboard capture — or 50 screenshots from 10 different apps. The AI processes each screenshot independently, applying the same column definitions to each one. It does not need to know which screenshot came from which application.
Step 3: Download the merged table. Every value the AI found for each column name is organized into rows — one row per screenshot. The column headers in your output file are the exact names you typed in Step 1. If a particular screenshot doesn't contain a value for a specific column (for example, the internal dashboard has no "Owner" field), that cell is left blank — the extraction continues for the remaining columns without error.
Files are processed securely and not stored.
How to Name Columns for Maximum Screenshot Extraction Accuracy
Column names carry more weight in screenshot extraction than they do in traditional document processing. A PDF invoice almost always uses predictable label text: "Invoice Number", "Due Date", "Subtotal". Screenshot UIs use whatever label the app designer chose — and across different apps, those labels vary wildly.
The naming principles below are specifically tuned for the screenshot-to-spreadsheet workflow, where the same column name must work across UIs designed by different teams with different conventions.
| Principle | Why It Matters for Screenshots | Example |
|---|---|---|
| Use the business concept, not the UI label | One app labels the field "Client", another labels it "Account Name", a third labels it "Customer Name". "Customer" as a column name covers all three because the AI matches the semantic role, not the literal string. | Column: Customer — matches "Client", "Account Name", "Bill To", "Customer Name" across different UIs |
| Disambiguate similar fields with qualifiers | Screenshots from CRMs and ERP systems often show multiple similar numbers. A deal card might show "Amount", "Expected Revenue", and "ARR" all on the same screen. "Deal Amount" is far more specific than "Amount". | "Deal Amount" instead of "Amount" — distinguishes the primary transaction value from annualized or forecast figures |
| Include a format hint when the output format matters | Different apps display dates differently: "03/15/26", "March 15, 2026", "2026-03-15". A format hint in the column name tells the AI to standardize regardless of how the source app displays it. | "Close Date (YYYY-MM-DD)" — normalizes all date formats to a single standard |
| Add context for values that appear as isolated numbers | Dashboard KPI cards often show numbers without nearby labels — just "47,200" in large font with a small "% change" subtitle. The AI needs enough semantic framing to match the number to the right column. | "MRR (Monthly Recurring Revenue, USD)" — gives the AI the context it needs when the UI provides minimal labeling |
A Reddit user working with over 600 screenshots from a field data collection app captured the naming problem succinctly: they needed "Tag Number, Length (cm), Weight (kg), Event Type, Tagger, Date, Time, GPS/Location, Water Temperature" from each image. The units in parentheses — "(cm)", "(kg)", "(°C)" — are not optional details. They're instructions that tell the AI which number is which when the UI shows multiple measurements on the same screen.
Batch Processing: One Column Definition, Any Number of Screenshots from Any App
Once your column names are defined, the workflow scales without additional configuration. This is where the investment in step one pays off: 5 screenshots or 500 — from 2 apps or 20 — the extraction specification doesn't change.
Consider the field researcher with 600 screenshots from a mobile data-logging app. Each screenshot contains the same types of data (tag number, length, weight, GPS coordinates), organized in the same UI layout on the same app. That's the easy case — same app, same layout, just high volume. Custom column extraction handles this, but so would a well-configured OCR template.
The harder case — and the one that makes custom column extraction the right architecture — is when the screenshots come from different applications. An operations manager collecting weekly metrics might capture:
- Revenue and order count from a Shopify admin panel (e-commerce dashboard layout)
- Pending shipments from a ShipStation screen (logistics table layout)
- Customer support ticket volume from a Zendesk dashboard (analytics card layout)
- Bank balance from a Chase mobile app screenshot (banking app card layout)
Each screenshot looks completely different. Yet the columns — "Revenue", "Orders", "Pending Shipments", "Open Tickets", "Bank Balance" — find their values in every one. The AI reads each screenshot independently, understands what data it contains, and maps the matching values to the right columns. The output is a single table with one row per screenshot and exactly the columns you defined.
For a deeper walkthrough of the batch workflow — including naming conventions, result merging, and handling screenshots from multiple sources — see our guide on batch processing app screenshots into structured spreadsheets. If you're new to screenshot extraction altogether and want a broader foundation, start with how to get data from screenshots into Excel without typing.
For a dedicated tool that converts any screenshot into structured Excel data using the column-name approach described here, see our screenshot to Excel extraction tool.
Frequently Asked Questions
Does this work on screenshots that contain no tables — just cards or label-value pairs?
Yes. That's the scenario where custom column extraction provides the most value over table-detection tools. A payment confirmation is three label-value pairs stacked vertically. A CRM deal card is a visual card with 8-10 fields arranged in a non-tabular layout. A banking app transaction detail is a scrolling list of key-value rows. The AI finds the values you asked for regardless of whether the layout is a table, a card, a form, or a mixed arrangement — it's reading for meaning, not for grid structure.
What if two different numbers on the same screenshot could both match a column name?
This is why column name specificity matters. "Amount" is ambiguous — a Salesforce opportunity card might show the deal amount, the expected revenue, and the annual recurring revenue all on the same screen. "Deal Amount" is specific enough to disambiguate. If ambiguity remains, you can add a format or position hint: "Deal Amount (the largest dollar value on the card)" gives the AI enough framing to pick the right one. The more precise your column name, the less room for ambiguity.
Can I extract data from screenshots that include both text and numbers in unstructured layouts?
Yes, within reasonable limits. If a screenshot contains a paragraph of free text and somewhere within it is the data you need (for example, a chat message that says "please ship 50 units of SKU-4421 to 123 Main St"), the AI can extract the quantity, SKU, and address as separate columns — provided your column names are clear about what you're looking for ("Ship Quantity", "SKU", "Delivery Address"). The extraction is less reliable than structured UIs, but it works. For chat-based order extraction specifically, the same column-name approach applies.
What accuracy should I expect from cross-app screenshot extraction?
Accuracy depends on three factors: input quality (clean, readable screenshots work better than blurry or cropped ones), column name specificity (vague names produce ambiguous matches), and the semantic clarity of the UI (an app that labels fields clearly produces better results than one that buries values in dense text). In practice, well-named columns on clear screenshots achieve high accuracy — often above 95% for straightforward fields like amounts, dates, and names. Complex or heavily abbreviated UIs may require occasional manual spot-checks, which is why the output table is a standard spreadsheet you can edit directly.
Does this approach work for screenshots in languages other than English?
Column names should match the language of your target output, not necessarily the language of the screenshot. If your column is "Customer Name" and the screenshot is in German showing "Kundenname", the AI understands that these refer to the same concept. The semantic matching works across languages because it operates on meaning rather than string matching.
How is this different from Excel's built-in "Data from Picture" feature?
Excel's "Data from Picture" is a table detector — it looks for row-and-column grid structures and converts them into spreadsheet cells. It does well with clean, bordered tables. It does not work with card layouts, label-value pairs, dashboard KPIs, or any non-tabular UI. Custom column extraction has no requirement that the data be in a table — it finds values by semantic role anywhere on the page. The two approaches solve different problems.
The column names you define are the only setup you need — for one screenshot or a thousand, from one app or twenty.
Try it on your own screenshots. Type the fields you need, upload from any app, and see if 30 seconds replaces the copy-paste loop.