If you’ve ever dragged a formula down thousands of rows in Google Sheets—or worse, realized you forgot to update the formula when new transactions came in—you know how messy and error-prone spreadsheets can get.
That’s where ArrayFormulas come in.
Instead of copy-pasting the same formula row by row, you can write it once and let it automatically expand to all the rows you need. It’s like upgrading your spreadsheet with a little database intelligence—saving time, reducing errors, and helping you close the books faster.
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Why Finance Teams Should Care About Array Formulas
For accounting teams, every minute matters during the month-end close. Small inefficiencies—like broken formulas, inconsistent ranges, or missing rows—can easily add up to hours of rework.
ArrayFormulas fix that:
Less manual work: One formula handles the entire dataset.
Fewer errors: Every new row gets calculated automatically.
Cleaner spreadsheets: No need for copying and pasting formulas.
Faster close: You know the ArrayFormulas will always work, so you don’t waste time troubleshooting.
Think of it as a way to bring “set it and forget it” automation into your spreadsheets.
Real-Life Accounting Examples Using Array Formulas
Here are three practical ways ArrayFormulas can help finance teams streamline workflows and speed up the close.
1. Auto-Filling Event Dates for Trade Show Expenses
When you’re dealing with trade show expenses—airfare, hotels, meals, booth deposits—depending on the amounts, they often need to be recorded as prepaid and then released later when the event actually happens.
The challenge is that while each expense might be tagged with the trade show name, the raw source data usually doesn’t include the event dates themselves. That gap makes it hard to align expenses with the right accounting period unless you build a way to automatically pull in those dates.
In this example, we’ll keep things simple so you can see exactly how it works. We’re going to look at:
A list of raw transactions for different trade shows (airfare, hotels, meals, etc.).
A separate mapping table that ties each event tag to its official start and end dates.
How an ArrayFormula with XLOOKUP can automatically enrich the transaction list with event dates, so every expense lines up with the right period without manual editing.
=ARRAYFORMULA( XLOOKUP(E3:E, J:J, K:K, "") )
This formula tells Sheets to look at every Event Tag in column E, find it in the mapping table (J:J), and return the corresponding Event Start date from column K. Because it’s wrapped in an ARRAYFORMULA, it doesn’t just apply to one row—it fills the entire column at once.
After setting it up, I import a refreshed set of transactions that includes ten new trade show expenses. As soon as the table updates, the formula automatically extends itself to cover the new rows. No dragging, no copy-paste—the dates just appear.
👉 That’s the power of combining ARRAYFORMULA with XLOOKUP: your spreadsheet adapts as data grows, which means fewer errors and faster closes.
2. Summarizing Trade Show Spend with SUMIFS and MAP
Let’s continue building on the previous example. Imagine you don’t just want to see the line-by-line transactions—you also want a quick way to see how much you’ve spent in total on each event.
To do this, we can use a SUMIFS formula wrapped inside MAP with LAMBDA. We won’t go deep into explaining LAMBDA here—that’s a separate discussion—but at a high level:
MAP takes each event name in the column and runs a small formula for it.
LAMBDA is just that small formula—it defines what we want to calculate for each event.
Inside the LAMBDA, SUMIFS adds up the Amount column, filtered by the Event Tag column, so it gives us the total spend for that specific event.
Here’s what the formula looks like:
=MAP(J3:J, LAMBDA(event, SUMIFS(D:D, E:E, event)))And here’s how to read it:
J3:J→ the list of event names we want totals forLAMBDA(event, …)→ defines the per-event calculationSUMIFS(D:D, E:E, event)→ sums the Amount column (D:D) where the Event Tag column (E:E) matches that specific event
3. A Quick Tip: Stop Extra Zeros in Google Sheets with ARRAY_CONSTRAIN
When you start using formulas like MAP or ARRAYFORMULA, you’ll notice they sometimes keep spilling results all the way down the column—even past where your data stops. That’s not wrong, but it can leave you with a bunch of empty zeros that don’t look very clean.
That’s where ARRAY_CONSTRAIN comes in.
It lets you “cap” the size of the array output so it only fills the number of rows you want. For example:
=ARRAY_CONSTRAIN(
MAP(J3:J, LAMBDA(event, SUMIFS(D:D, E:E, event))),
COUNTA(J3:J),
1
)Here’s what’s happening:
MAP(...)→ creates the totals for each eventCOUNTA(J3:J)→ counts how many event names you actually have1→ the number of columns to return
The result is a tidy list of totals that stops exactly where your event list ends.
👉 You don’t always need ARRAY_CONSTRAIN, but it’s a handy way to keep formulas from running endlessly down your sheet.
3. Building a Scalable Revenue Recognition Table in Google Sheets with ArrayFormulas
When you build a revenue recognition matrix, you usually drop a formula into each month cell that says “only show the monthly amount if this month is inside the contract window”:
=IF(AND(G$1>=$D2, G$1<=$E2), $F2, 0)G$1= the month header (e.g., 1/31/2025)$D2/$E2= Start / End date for the contract$F2= Monthly amount
This works, until it doesn’t. With thousands of rows, someone will insert a row, forget to copy formulas, or miss a new invoice in the middle.
3.1 Automating the Whole Revenue Matrix with MAKEARRAY
For this example, we’re keeping it intentionally simple: no rounding, no daily allocations, and no credit notes—just a clean monthly split across the contract window. The goal is to replace thousands of fragile cell-by-cell IFs with one array formula that builds the entire recognition table at once.
What this does (at a high level):
Reads your Start Date, End Date, Monthly Amount per contract.
Looks at your month headers (e.g., Jan–Dec).
Fills each cell with the monthly amount if that header month is inside the contract window; otherwise it writes
0.Spills the full matrix automatically—no dragging, no copy/paste.
=ARRAY_CONSTRAIN(
MAKEARRAY(
COUNTA(A2:A),
COLUMNS(G1:AA1),
LAMBDA(r,c,
IF(
(INDEX(G1:AA1,1,c) >= INDEX(D2:D,r)) *
(INDEX(G1:AA1,1,c) <= INDEX(E2:E,r)),
INDEX(F2:F,r),
0
)
)
),
COUNTA(A2:A),
COLUMNS(G1:AA1)
)
What does MAKEARRAY do? MAKEARRAY(rows, cols, LAMBDA(r, c, ...)) literally creates a grid (rows × columns).For each cell in that grid, it calls the LAMBDA, passing in:
r= the row index (which corresponds to the contract number)c= the column index (which corresponds to the month header)
So instead of filling each cell manually, you define the logic once, and Sheets applies it to every row/column intersection.
How the condition works inside the LAMBDA? We check whether the month header belongs to the contract period:
IF(
AND(
INDEX(G1:AA1,1,c) >= INDEX(D2:D,r),
INDEX(G1:AA1,1,c) <= INDEX(E2:E,r)
),
INDEX(F2:F,r),
0
)
INDEX(G1:AA1,1,c)picks the c-th column header (e.g., Feb 2025).INDEX(D2:D,r)andINDEX(E2:E,r)pick the r-th contract’s start and end dates and enter the monthly amount.If the header month is between those dates, output the monthly revenue; otherwise, 0.
That means every cell in the matrix answers the question: “Does this month fall inside this contract’s window?”
And if you’re working with giant Google Sheets that start to feel sluggish, switching to a single MAKEARRAY formula can improve performance significantly compared to thousands of individual formulas.
If you want the real-world version (daily proration, start/end month rounding, partial months, credit notes, renewals), I’ll publish a deeper walkthrough soon. Subscribe to get the advanced template when it drops.
Summary
ArrayFormulas are one of the simplest upgrades you can make to your Google Sheets workflows. Instead of relying on thousands of fragile, copy-pasted formulas, you can define logic once and let it apply across your entire dataset.
The result: cleaner spreadsheets, fewer errors, and a faster close.
👉 If you’re working with large or complex Google Sheets, shifting to array-driven formulas doesn’t just save time—it can also improve performance significantly compared to maintaining thousands of individual formulas.