

Every sales or marketing sheet hides a quiet villain: rows that should not be there. Wrong stages, stale leads, mis‑tagged campaigns. In Google Sheets, the not equal operator (<>), and its twin function NE(value1,value2), are how you teach your data to say, “this does not belong.” Whenever NE() returns TRUE, it’s your signal that value1 and value2 diverge.Instead of hunting those mismatches cell by cell, you can let an AI computer agent sit between your tools and Google Sheets. The agent applies <> rules as it pastes in exports, reconciles CRM data, and cleans campaign reports. You describe what “not equal” means for your business—“status <> ‘Qualified’”, “channel <> ‘Paid Search’”—and the agent tirelessly enforces that logic, hour after hour, so your team only works with the rows that actually deserve their time.
If you run a sales team, agency, or lean SaaS company, your Google Sheets are full of tiny decisions powered by one quiet symbol: `<>`, the “not equal” operator. It’s how you tell Sheets, “show me everything that doesn’t match.” Used well, `<>` becomes a filter for bad leads, off‑strategy spend, or broken data feeds.Below is a practical guide to using the not equal operator manually, with no‑code automation, and finally at scale with an AI agent that can work across your entire desktop and browser.## 1. Manual ways to use the not equal operator in Google Sheets### 1.1 Basic comparisons with `<>` and NE()Google Sheets supports two equivalent ways to check inequality:- The operator: `=A2<>B2`- The function: `=NE(A2,B2)`According to Google’s official docs, `NE(value1,value2)` returns TRUE if the values are not equal and FALSE if they are equal: https://support.google.com/docs/answer/3093981**Steps to compare two cells:**1. Click an empty cell, e.g., `C2`.2. Type `=A2<>B2`.3. Press Enter. You’ll see TRUE if the values differ, FALSE if they match.4. Drag the fill handle down to check each row.This is perfect for checking whether imported deal values differ from CRM values, or if two versions of a pricing sheet got out of sync.### 1.2 Build business rules with IF + `<>`You can turn inequality checks into decisions using IF:`=IF(A2<>"Won","Follow up","Archive")`**Step‑by‑step:**1. In your pipeline sheet, insert a new column called `Next Action`.2. In `D2`, type the formula above, adjusting `A2` and the status text to match your sheet.3. Copy the formula down.Now any row where the status is not equal to "Won" gets a clear instruction. For an agency, swap this for campaign status, e.g., `<>"Active"` to flag campaigns needing attention.### 1.3 Filter rows with `<>` using FILTER()The FILTER function lets you return **only** rows that pass a condition. Combined with `<>`, it becomes a live, auto‑updating view of “everything except X”. Official FILTER docs: https://support.google.com/docs/answer/3093197Example: show all deals where the owner is **not** "Agent1":`=FILTER(A2:E100, E2:E100<>"Agent1")`Steps:1. Create a new tab called `Non_Agent1_Deals`.2. In `A1`, paste the formula above, changing the range to your table.3. Every time data changes, this view updates automatically.For a marketing team, swap owners for channels: `F2:F100<>"Paid Search"` to analyse only non‑search performance.### 1.4 Use `<>` in QUERY for analyticsQUERY is like SQL inside Sheets (docs: https://support.google.com/docs/answer/3093343).To count all tickets **not** handled by "Agent1":`=QUERY(A1:E100, "select count(E) where E<>'Agent1'", 1)`Steps:1. In an empty cell, type `=QUERY(`.2. Select the table range.3. Inside the query string, write a clause with `<>`, like `where E<>'Agent1'`.4. Close the parenthesis and press Enter.This is great for performance reviews, channel mix analysis, or “all leads not from partner X”.### 1.5 SUMIF/COUNTIF with “does not equal” criteriaYou can also sum or count only values that do **not** match something. Docs for SUMIF: https://support.google.com/docs/answer/3093583Examples:- Sum revenue from all sources except "Referral": `=SUMIF(C2:C100,"<>Referral",D2:D100)`- Count leads whose status is not "Qualified": `=COUNTIF(B2:B100,"<>Qualified")`Remember: in SUMIF/COUNTIF, the `<>` must go inside quotes.## 2. No‑code automation methodsOnce you’re comfortable manually using `<>`, the next step is to stop doing it by hand.### 2.1 Use Google Sheets filters and viewsYou can turn `<>` logic into reusable views without formulas:1. Select your header row and go to **Data → Create a filter**.2. Click the filter icon on the column you care about.3. Use **Filter by condition → Is not equal to** and enter a value like `Won`.4. Save this as a **Filter view** (Data → Filter views → Save as filter view) named `Non‑Won Deals`.Now your team can instantly switch to this view whenever they want to focus on open opportunities.### 2.2 Automation with Apps ScriptIf you’re slightly technical—or have someone on your team who is—you can use Google Apps Script to automatically clean rows using `<>` rules on a schedule.Example idea:- Every night, a script scans your sheet.- Any row where `Status <> "Won"` and `Last_Contact_Date` is older than 14 days gets highlighted or added to a follow‑up tab.High‑level steps:1. In Sheets, go to **Extensions → Apps Script**.2. Write a script that loops over rows and checks conditions with `!=` in JavaScript (equivalent to `<>` in formulas).3. Use the Triggers menu to run it every hour or day.This moves you from “I check manually” to “Google runs the rule for me.”### 2.3 Connectors and no‑code toolsTools like Zapier, Make, or native CRM→Sheets connectors can apply `<>` logic before data ever hits the sheet.Examples:- Send leads to a “review” sheet only if `Lead_Source <> 'Paid'`.- Log support tickets to a VIP sheet only if `Customer_Tier <> 'Free'`.You configure conditions inside the no‑code tool’s UI (often as “is not equal to”), and it handles the rest.## 3. Scaling with an AI agent (Simular)Manual and no‑code methods break down once you have dozens of sheets, multiple tools, and constant exports. That’s where an AI computer agent like Simular comes in.Simular Pro is a highly capable computer‑use agent that can operate across your entire desktop and browser, just like a human, but with production‑grade reliability (https://www.simular.ai/simular-pro).### 3.1 Let the agent enforce `<>` rules across many sheetsImagine you have:- A CRM export- An ad platform export- A billing exportYou want to keep a central revenue sheet where:- `Channel <> 'Internal'`- `Status <> 'Test'`- `Country <> ''` (no blanks allowed)Instead of manually massaging each file:1. You describe the workflow once to Simular: open the files, paste into a master Google Sheet, apply formulas using `<>` and NE(), and remove or tag rows that break your rules.2. The agent follows your instructions, step by step, across browser and desktop.3. Because Simular exposes every action transparently, you can inspect how it applied each not equal rule.**Pros:**- Works across multiple tools (Sheets, CRM, ad platforms, email).- Handles thousands or millions of steps reliably.- Easy to adjust rules without rewriting code.**Cons:**- Initial onboarding takes a bit of thought.- Best suited once you have recurring, high‑volume workflows.### 3.2 Use webhooks and Simular for continuous hygieneSimular integrates into production pipelines via webhooks. That means you can trigger the agent whenever a new CSV lands in Drive or a CRM export finishes.Example workflow for an agency:1. New campaign report is saved.2. A webhook pings Simular.3. The agent opens the report, pushes data into Google Sheets, and applies `<>` rules to: - Exclude `Campaign_Status <> 'Active'` from dashboards. - Flag `Cost_Per_Lead <> 0` but missing revenue.Over time, this becomes the quiet, always‑on assistant that keeps your Sheets honest.### 3.3 When to graduate to an AI agentIf you:- Spend hours per week reconciling exports- Maintain many similar Sheets for different clients or regions- Frequently change business rules like “include everything except X”…then it’s time to let an AI agent own the entire workflow. You stay focused on defining the `<>` rules and the outcomes; Simular handles the clicks, filters, copies, and cleanups.For more on how Simular thinks about autonomous computer agents and reliability, see https://www.simular.ai/about.
To compare two lists in Google Sheets with the not equal operator, set up a helper column that checks row‑by‑row differences. Suppose list A is in column A and list B is in column B.1. Insert a header in C1 like "Match?".2. In C2, enter `=A2<>B2` and press Enter.3. Drag the fill handle down to copy the formula for all rows.4. TRUE means the values in that row are different, FALSE means they match.To highlight only mismatches, wrap this in IF:`=IF(A2<>B2,"Mismatch","OK")`.If your lists are different lengths or out of order, first align them (e.g., by sorting both ranges by the same key). For complex scenarios, use `=NE(A2,B2)` instead of `<>`—it behaves the same but can be clearer when nesting in longer formulas. This approach is ideal for checking CRM vs. billing exports or mapping two client lists.
To filter rows based on "does not equal" logic, you can use either the FILTER function or built‑in filter views.**Using FILTER():**If your table is A1:E100 and you want all rows where column C is not equal to "Won", use:`=FILTER(A2:E100, C2:C100<>"Won")`1. Go to a new tab.2. In A1, paste the formula and adjust ranges/criteria.3. The result is a live view of only non‑Won rows.**Using the UI:**1. Select the header row.2. Click **Data → Create a filter**.3. Click the filter icon on column C.4. Choose **Filter by condition → Is not equal to**.5. Enter `Won` and apply.For reusable setups, save this as a filter view (Data → Filter views → Save). Use "Is not equal to" whenever you need a working list of open deals, non‑active campaigns, or any “everything except X” view.
Using NE() or <> inside IF turns comparisons into clear decisions. The syntax is:`=IF(condition, value_if_true, value_if_false)`.With the operator:`=IF(A2<>"Qualified","Follow up","Archive")`Here, if A2 is not equal to "Qualified", the cell shows "Follow up"; otherwise it shows "Archive".With the function version:`=IF(NE(A2,B2),"Different","Same")`This returns "Different" when A2 and B2 are not equal.Steps:1. Decide what you’re checking and what actions you want.2. In an empty column, type an IF formula using either `<>` or `NE()`.3. Press Enter and drag down to fill.Use this pattern to assign sales tasks, tag problem rows, or drive dashboards. NE() can be easier to read in long formulas, while `<>` is shorter and required inside QUERY clauses.
To use "does not equal" with SUMIF or COUNTIF, remember that the operator must be in quotes as part of the criteria string.**SUMIF example:** Sum revenue from all sources except "Referral":`=SUMIF(B2:B100,"<>Referral",C2:C100)`- B2:B100 is the range checked for the condition.- "<>Referral" means "source is not Referral".- C2:C100 is the range to sum.**COUNTIF example:** Count leads where status is not "Qualified":`=COUNTIF(D2:D100,"<>Qualified")`Steps:1. Identify the condition column and the metric column.2. In an empty cell, write SUMIF or COUNTIF with the criteria like "<>YourValue".3. Double‑check quotes—`<>` must be inside them, or Sheets will throw an error.You can combine wildcards too, e.g. `"<>*Test"` to exclude values ending with "Test". This is powerful for excluding test data, internal traffic, or non‑billable items from reports.
Manually applying not equal rules works for one sheet, but it doesn’t scale when you’re juggling exports from a CRM, ad platforms, payment providers, and internal tools. An AI computer agent such as Simular can sit above Google Sheets and handle the entire workflow that surrounds your `<>` and NE() formulas.Instead of you:- Downloading files- Cleaning rows where `Status<>"Won"` or `Channel<>"Paid"`- Pasting into dashboards…the agent opens your browser, your Sheets, and your folders, then executes those steps exactly as you would, but on a schedule and at scale. You define the business logic (“exclude test campaigns”, “flag leads not equal to ‘Qualified’”), and the agent keeps enforcing it.This frees sales, marketing, and ops teams from repetitive hygiene work so they can focus on strategy—while knowing every new dataset is automatically filtered by the same, reliable not equal rules.