TL;DR for Product Managers
Spreadsheets are still the fastest way to answer a question no existing dashboard was built to answer. For product managers specifically, the combination of Google Sheets for live collaboration and Airtable for structured roadmap data covers about 80% of day-to-day analytical needs. Nail those two before adding anything else to your stack.
What Product Managers Actually Need To Track
You are not a data engineer. You should not be building ETL pipelines or maintaining a warehouse. But you do need to make decisions fast, and that means having a short list of metrics that actually matter to your product and knowing exactly where to pull them from.
Here are seven things most PMs end up tracking in spreadsheets every single week.
Feature adoption rate. What percentage of active users triggered a new feature within 30 days of launch? This one number tells you more than any NPS score about whether your last sprint shipped real value.
Time-to-value (TTV). How many days pass between a user signing up and completing their first meaningful action? If TTV creeps up after an onboarding change, something in your new flow is creating friction.
Retention by cohort. Week 1, Week 4, and Week 12 retention for each cohort of users acquired in a given month. A spreadsheet cohort table lets you spot churn curves that aggregated dashboards hide completely.
Backlog age distribution. How old are the tickets sitting in your backlog right now? A simple histogram bucketed by weeks tells you whether your backlog is a living document or a graveyard nobody maintains.
Sprint velocity and scope creep rate. Planned story points versus delivered, tracked over 8 to 12 sprints. Patterns here predict your next quarter’s delivery accuracy better than any gut estimate from an engineering lead.
Stakeholder ask volume. A running log of ad-hoc requests coming in from sales, support, and leadership. This is rarely tracked and almost always useful when you need to justify headcount or say no to something with evidence.
Revenue impact per feature. Which features correlate with expansion MRR or reduced churn? Even a rough attribution model in a spreadsheet, linked to a CRM export, changes the conversation in quarterly planning meetings.
Most of these do not require a full business intelligence tool. A well-structured spreadsheet updated twice a week answers 90% of the questions your leadership team will throw at you. For a deeper breakdown of which metrics matter at each growth stage, check out our guide on product analytics for early-stage startups.
The Practical Tool Stack
The tools below are chosen because product managers actually use them, not because vendors pay to be recommended.
Google Sheets
Google Sheets is the default collaboration layer for any PM working with a cross-functional team. It is free inside a Google Workspace account, with Workspace Business Starter starting around $6/user/month.
What makes it specifically useful for product managers is real-time collaboration combined with the IMPORTRANGE function. You can pull data from your growth team’s acquisition sheet directly into your retention model without anyone emailing you a CSV. It also connects cleanly to Looker Studio for one-click visualisation when you need to present findings to leadership.
Microsoft Excel
Microsoft Excel is the right call when you need Power Query to clean messy data exports from your CRM or ticketing tool. A Microsoft 365 Business Basic subscription starts around $6/user/month.
For PMs, the standout feature is Power Query’s ability to reshape and merge tables from different sources without writing a single line of code. If you regularly export raw data from Jira, Salesforce, or Zendesk, learning Power Query will save you two to four hours every week. Pivot tables in Excel also handle larger datasets more smoothly than Sheets when you are working with tens of thousands of rows.
Airtable
Airtable sits between a spreadsheet and a lightweight database. The free plan supports up to 1,000 records per base. Paid plans start around $20/user/month.
For product managers, Airtable is best used as a living roadmap and feature request tracker. You can link tables (connecting customer requests to features, and features to sprints) in a way Google Sheets cannot do natively. The grid view looks like a familiar spreadsheet, so stakeholders who get nervous at the word “database” are comfortable working in it.
Mixpanel
Mixpanel is an event-based product analytics platform. There is a free plan up to 20 million monthly events. Paid plans start around $28/month.
You will not run Mixpanel inside a spreadsheet. But you will export segments and funnel data from Mixpanel into Sheets constantly. Knowing how to structure that export and how to build cohort retention tables from raw event data is one of the highest-leverage spreadsheet skills a PM can develop. Our guide to Mixpanel exports for non-engineers covers the exact steps.
Looker Studio
Looker Studio (formerly Google Data Studio) is Google’s free dashboard tool. It connects directly to Sheets, BigQuery, and dozens of other sources at no cost.
For product managers who need to present metrics to leadership every week, Looker Studio removes the “update the chart manually” step. You build the dashboard once, connect it to your live Sheet, and it refreshes automatically. The presentation-quality output means you can skip the “copy chart into slides” step entirely.
Notion
Notion with its database views has become a practical alternative to Airtable for smaller teams. A free plan is available. Paid plans start around $12/user/month.
Where it fits in a PM’s stack is in the documentation layer. Sprint retrospectives, feature specs, and meeting notes in Notion can link to database entries covering features, bugs, and experiments. It is not a spreadsheet replacement but it reduces the number of things you track in spreadsheets unnecessarily, which keeps your sheets cleaner.
A Realistic Weekly Workflow
Here is what a functional weekly spreadsheet habit actually looks like. Not aspirational. Actual.
Monday morning. You open your Mixpanel dashboard and export last week’s funnel data. Paste the CSV into your master retention Sheet. Your SPARKLINE formulas update automatically, giving you a quick visual read on whether Week 1 and Week 4 retention moved. If a number looks off, you filter by cohort and find the anomaly before standup.
Tuesday. Backlog grooming day. You open your Airtable base, sort by “last updated” date, and archive anything older than 90 days with zero customer votes attached. You update the sprint column on items moving into the next cycle. You run a quick COUNTIF in your linked Sheet to show the backlog age distribution across teams.
Wednesday. Stakeholder requests tend to come in hardest mid-week. You log each ad-hoc ask in a running Google Sheet with the requestor name, the request description, the estimated effort, and whether it maps to a current roadmap theme. At the end of the quarter, this log becomes your evidence for headcount conversations.
Thursday. You pull this week’s sprint data from Jira using the CSV export. In Excel, you run Power Query to join it with your sprint history table and update the velocity chart. You check scope creep by comparing the count of items tagged “added mid-sprint” to total tickets closed.
Friday. Fifteen minutes. You confirm the Sheet connected to your Looker Studio dashboard has this week’s numbers populated. You write three bullet points in a Notion doc summarizing what moved, what did not, and what needs a decision next week. You paste that into your weekly update Slack message and close the laptop.
Total active spreadsheet time: roughly two to three hours spread across five days. Enough to stay sharp without drowning in it.
Common Pitfalls In This Industry
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Tracking too many metrics at once. If you have more than 10 metrics in your weekly sheet, half of them are not being acted on. Cut anything you have not referenced in a decision in the last 30 days.
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Rebuilding the same table every sprint manually. If you are copying and pasting data by hand each week, you need either an IMPORTRANGE formula or a scheduled export. Manual rebuilds introduce errors and erode trust in your numbers.
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Mixing raw data and analysis in the same tab. Keep raw exports on a dedicated tab. Build your formulas on a clean analysis tab that references the raw tab. When the next export comes in, you replace the raw data and nothing breaks in your calculations.
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Sharing edit access to your master sheets. Stakeholders who can edit the sheet you use for analysis will break your formulas. Share view-only links or export a PDF for presentations. Protect your key tabs.
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Skipping the data dictionary. A sheet with columns named “V2_FINAL_USE THIS” and “metric_old_backup” is unusable in six months. Spend 30 minutes writing a one-tab glossary with each column name, its source, and exactly how it is calculated.
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Using spreadsheets for things databases should handle. When your feature request tracker hits 2,000 rows and requires five people editing simultaneously, a spreadsheet is the wrong tool. Airtable or a lightweight database will serve you better and save hours of merge conflict cleanup.
When To Hire An Analyst Or Agency
The signal that DIY spreadsheet work is no longer enough is usually not a data volume problem. It is a decision latency problem.
If your leadership team is waiting more than 48 hours for answers to data questions and you are the bottleneck, you need analytical capacity. You can bring in a fractional analyst (typically $3,000 to $6,000 per month for roughly 20 hours a week) or hire a junior data analyst (typically $55,000 to $75,000 annually in most markets) who owns the data layer so you can focus on product decisions.
An agency makes sense when you need a full data stack stood up quickly. Warehouse, BI tool, and automated reporting, deployed in four to six weeks. Expect $15,000 to $40,000 for that engagement depending on scope.
The trigger to act is when you notice yourself spending more than six hours a week on data wrangling rather than on product decisions. That ratio is costing your company more than a hire would cost.
Browse our excel-sheets-power-skills guides for deeper coverage on when to automate versus when to bring in outside help.
Frequently Asked Questions
Do product managers really need spreadsheet skills or is SQL more important?
Both matter, but for different moments. SQL gets you to raw data faster. Spreadsheets let you model and present that data without writing more queries every time you want to look at it differently. Most PMs find that intermediate spreadsheet skills plus basic SQL covers the full workflow without requiring a data engineering background.
What is the single most useful spreadsheet function for a product manager?
VLOOKUP and INDEX/MATCH come up often, but the real workhorse is SUMIFS. Being able to slice totals by multiple conditions at once (cohort, feature flag, plan tier) without building a pivot table is something you will use every week once you learn it.
How do I stop my roadmap sheet from turning into a mess?
Use one tab per data type: one for raw inputs, one for the roadmap view, one for the stakeholder log. Add a “last updated” column to every row. Agree on a naming convention for items before you start, not after 200 rows are already in there.
Can I do cohort analysis in Google Sheets without a dedicated analytics tool?
Yes, if you have a user-level data export with signup date and activity dates. You use a combination of EOMONTH and COUNTIFS to build a cohort matrix manually. It gets tedious beyond six months of data but it is completely doable and a good learning exercise. Our walkthrough at cohort analysis in Google Sheets covers the exact formula setup.
When should a PM use Airtable instead of Google Sheets?
Use Airtable when your data has relationships. Features linked to epics, linked to customer requests, linked to sprint cycles. Sheets handles flat data well. Once you need to connect records across tables and filter by those relationships, Airtable is the faster path and the one that stays maintainable as the dataset grows.
Bottom Line
The single most useful thing you can do this quarter is build one clean, well-structured Google Sheet that tracks your five most important product metrics and takes under 30 minutes to update each week. Not a dashboard. Not a multi-tab monster. One focused sheet with a raw data tab, an analysis tab, and a chart tab.
That sheet will answer more questions in your next planning meeting than any slide deck you have built this year. It will also show your leadership team that you operate from evidence rather than instinct, which changes how your proposals land when resources are tight.
Start there. Add the other tools as the need becomes obvious rather than building the whole stack upfront.
For more practical guides on spreadsheet skills, workflows, and tool comparisons built for analysts and product managers, visit /category/excel-sheets-power-skills/.