Google Sheets for PMs: Beyond Basic Spreadsheets

The PM's Secret Weapon

Every PM has a dedicated product management tool. But when they need to actually think through a problem, model a scenario, or make a quick decision, they open Google Sheets.

Sheets is the Swiss Army knife of product management. It is not the best tool for any single task, but it is the fastest tool for most tasks. Need to score 20 features by tomorrow? Sheets. Need to model pricing tiers? Sheets. Need to summarize 200 customer feedback entries? Sheets. Need to plan team capacity for the next quarter? Sheets.

The PMs who get the most from Google Sheets go beyond basic data entry. They use formulas, pivot tables, conditional formatting, and cross-sheet references to build reusable tools that save hours every sprint cycle.

RICE Scoring Spreadsheet

RICE scoring is one of the most widely used prioritization frameworks, and a well-built spreadsheet makes it practical for real backlog prioritization.

The spreadsheet structure

Create a sheet with these columns:

Making it useful

Add data validation dropdowns for Impact and Confidence so the team uses consistent values. Color-code the RICE Score column with conditional formatting: green for scores above your threshold, yellow for borderline, red for low-priority.

Sort by RICE Score descending. The top of the list is your prioritized backlog. But do not treat the scores as absolute truth. RICE provides a starting point for discussion, not a final answer. Use IdeaPlan's RICE calculator to validate individual scores and check the math before presenting to stakeholders.

Beyond basic RICE

Add columns for strategic alignment and dependencies:

• Strategic Theme (Select): Which company objective does this serve? (Growth, Retention, Platform)
• Dependencies (Text): What needs to happen before this can start?
• Status (Select): Not Started, In Discovery, Ready for Dev, In Progress, Shipped

Filter by theme to see how your backlog aligns with strategic priorities. Filter by status to see your pipeline at a glance.

Pricing Model Templates

Pricing decisions involve modeling multiple scenarios with different assumptions. Spreadsheets are the right tool for this.

Revenue model structure

Create three sheets:

Sheet 1: Assumptions

Monthly new signups: 500
Free-to-paid conversion rate: 8%
Monthly churn rate: 3%
Average expansion rate: 5%

Sheet 2: Pricing tiers

Sheet 3: Revenue projection

Build a 12-month projection using formulas that reference the assumptions sheet:

• New MRR = New Signups Conversion Rate Weighted Average Price
• Expansion MRR = Previous Month MRR * Expansion Rate
• Churned MRR = Previous Month MRR * Churn Rate
• Net New MRR = New MRR + Expansion MRR - Churned MRR
• Total MRR = Previous Month Total MRR + Net New MRR

Scenario modeling

The power of this setup is scenario analysis. Duplicate the Assumptions sheet three times:

• Conservative: Lower conversion rate, higher churn
• Base: Your best estimates
• Optimistic: Higher conversion rate, lower churn

Use INDIRECT() or named ranges to let Sheet 3 switch between scenarios with a single dropdown. Present all three scenarios to leadership instead of a single forecast.

Capacity Planning

Capacity planning in a spreadsheet beats capacity planning in your head every time.

The capacity spreadsheet

Step 1: Calculate available capacity

Subtract holidays, PTO, and on-call rotations from the raw days. Multiply by allocation percentage. Sum the Effective Days column for total team capacity.

Step 2: Size your roadmap items

Step 3: Compare capacity to demand

Add a running total column. When the running total exceeds your team's capacity, draw the line. Everything above the line fits in the quarter. Everything below does not.

This is a simple model, but it prevents the most common PM mistake: committing to more work than the team can deliver. Share this sheet with engineering leads before finalizing the quarterly plan.

OKR Tracking

OKRs need a tracking system. Dedicated OKR tools exist, but for teams under 50 people, a well-structured spreadsheet is often more practical and gets better adoption.

The OKR spreadsheet structure

Create one sheet per quarter with this layout:

Formulas that help

• Progress: = (Current - Baseline) / (Target - Baseline). Shows percentage toward the target, not just the current value
• Conditional formatting on Confidence: Green for "On Track", Yellow for "At Risk", Red for "Off Track"
• Sparklines: =SPARKLINE(range) in the Notes column to show weekly trend data inline

The weekly check-in process

Every Monday, each key result owner updates the Current value and Confidence column. The PM reviews all OKRs, identifies any that slipped from "On Track" to "At Risk," and follows up with the owner. This takes 15 minutes per week and keeps the entire team aligned on what matters.

Pivot Tables for Customer Feedback Analysis

When you have 200 pieces of customer feedback in a spreadsheet, pivot tables are the fastest way to find patterns.

Setting up the feedback sheet

Structure your raw feedback with consistent columns:

Building the pivot table

Insert a pivot table (Data > Pivot table) and configure:

• Rows: Category
• Columns: Customer Segment
• Values: Count of feedback entries

This instantly shows you which categories have the most feedback and which customer segments are most affected. Switch Values to SUM of Revenue Impact to see which issues represent the most revenue.

Advanced pivot table analyses

Feedback trend by month:

• Rows: Category
• Columns: Date (grouped by month)
• Values: Count

This shows whether specific issues are growing or shrinking over time. A spike in "Reporting" feedback in February might correlate with a recent release.

Top requests by segment:

• Filter: Customer Segment = "Enterprise"
• Rows: Subcategory
• Values: Count, sorted descending

This shows what your enterprise customers care about most, which directly informs your prioritization for that segment.

IMPORTRANGE for Cross-Team Dashboards

If your organization uses multiple spreadsheets across teams, IMPORTRANGE connects them without requiring everyone to work in the same file.

How it works

=IMPORTRANGE("spreadsheet_url", "Sheet1!A1:D50")

This pulls live data from another spreadsheet. Use it to:

• Aggregate OKRs across teams. Each team maintains their own OKR sheet. A master sheet pulls current values from all team sheets into a single view.
• Combine feedback from multiple sources. Support team has one feedback sheet, sales team has another. Pull both into a combined analysis sheet.
• Create executive dashboards. Pull key metrics from engineering's velocity sheet, support's ticket volume sheet, and your product metrics sheet into one dashboard.

Practical tips

• Grant access first. The first time you use IMPORTRANGE from a new source, you need to authorize the connection. Do this manually before sharing the master sheet with others.
• Minimize imported ranges. Import only the columns you need, not entire sheets. Large IMPORTRANGE calls slow down the spreadsheet.
• Add a "Last Updated" cell. Include a =NOW() formula on each source sheet so the master dashboard shows when data was last refreshed. If a source has not been updated in two weeks, the data is stale.

Templates and Formulas Worth Knowing

A few more techniques that save PM time:

Conditional formatting for status boards

Select your status column, create conditional formatting rules:

• = "On Track" → Green background
• = "At Risk" → Yellow background
• = "Off Track" → Red background

This turns any tracking sheet into a visual dashboard without building a separate dashboard tool.

VLOOKUP for connecting data

When your feature list is in one sheet and customer feedback is in another, use VLOOKUP to connect them:

=VLOOKUP(A2, FeedbackSheet!A:D, 4, FALSE)

This pulls related data from another sheet based on a shared key (feature name, customer ID, etc.).

Named ranges for clarity

Instead of referencing B2:B50 in every formula, name the range RICE_Scores. Your formulas become =AVERAGE(RICE_Scores) instead of =AVERAGE(B2:B50). This makes complex spreadsheets readable by anyone, not just the person who built them.

Data validation for consistency

For any column where you want consistent values (Status, Priority, Category), add data validation with a dropdown list. This prevents the "Is it 'High Priority' or 'P1' or 'Urgent'?" problem that makes filtering and pivot tables unreliable.

When to Move Beyond Sheets

Google Sheets is the right tool until it is not. Consider moving to a dedicated tool when:

• More than 5 people need to edit simultaneously. Sheets handles concurrent editing but gets slow and conflict-prone beyond a few editors.
• You need audit trails. Who changed what and when? Sheets' version history is basic. Dedicated tools track changes at the field level.
• The spreadsheet has more than 10 tabs. At this point, you have built an application in a spreadsheet. It is time for a real tool.
• Non-technical stakeholders need to interact with it. If executives or customers need to view the data, a dashboard tool or presentation is more appropriate than a shared Sheet.

For many PM workflows, the right answer is to prototype in Sheets, validate the workflow, and then migrate to a dedicated tool only if the sheet becomes a bottleneck. You can start with a RICE scoring sheet and graduate to a dedicated tool when the process is proven. See IdeaPlan's quarterly roadmap Google Sheets template for a production-ready starting point.