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Excel remains central in commercial operations because it converts imperfect inputs into workable decision models quickly. For sales leaders in small to medium sized businesses and lean team, that speed matters because reporting cycles are frequent, data lives across multiple tools, and there is often limited analytics support. Even when a team has Power BI or another BI layer, the last mile of forecasting, gap analysis, territory focus, and board reporting frequently happens in Excel.
This pattern shows up clearly in adjacent functions like finance and planning. In the 2024 FP&A Trends Survey, Excel was reported as the most widely used planning application, at 52% of organizations surveyed. In a 2024 PayEm survey, 86% of financial professionals reported relying on Excel for budgeting and forecasting. This helps explain why Excel remains the default environment for work that requires fast iteration on definitions and assumptions.
Why sales workflows often end up in Excel's pivot tables
Even when a company has a business intelligence (BI) tool, many sales teams still end up in Excel for the same reason people still sketch on paper after buying expensive design software: the “final thinking” is messy, interactive, and changes minute to minute. Tools like Power BI, Tableau, Looker, Qlik, Sigma, and Mode are excellent at showing the numbers that already have definitions. They are built to answer questions like “What were bookings last month?” or “How is pipeline trending by region?” They work best when the underlying data is clean, the categories are consistent, and everyone agrees on what each metric means.
Sales leaders often need something different. Your job is not just to view results, but to make decisions from imperfect data, under time pressure, with exceptions that are hard to standardize. They are trying to answer questions like: “Which deals are real?” “Where are we short?” “Which reps need help?” “What should we change this week so we hit the quarter?” Those questions rarely have one stable definition, and the answer often depends on judgment and changing assumptions.
That is where Excel comes in. Excel is not just a place to store data. It is a flexible workspace where someone can quickly test a rule, adjust a threshold, and immediately see what changes. You can say, “Show me deals in late stages that have not moved in 21 days,” then change 21 to 14, exclude a specific segment, add a filter for discounting, and create a short list of accounts to review. That kind of fast back-and-forth is harder in BI tools because BI tools are designed for consistency and governance. Changing logic often means changing the report, the filters, or even how the data is structured. That takes time, and sales decisions usually cannot wait.
Pivot tables are the shortcut that makes Excel especially attractive. A pivot table is essentially a fast way to “rotate” the same data to see it from different angles without rebuilding the whole analysis. In a few clicks, the same list of deals can be viewed by rep, by region, by deal stage, by close month, by product, or by account size. In sales meetings, questions evolve live. Someone asks, “Is this just one region?” then “Is it one rep?” then “Is it only enterprise?” then “Is it mostly renewals slipping?” Pivot tables make those follow-up questions cheap to answer in real time.
There is also a practical reason that has nothing to do with technology. Many sales leaders don't have a dedicated analyst they can delegate to. When your team asks for a revised forecast narrative by the end of the day, exporting to Excel is the fastest way to control the logic personally and produce an answer they can stand behind. Excel becomes the “decision workbench” because it is accessible, fast, and does not require waiting for someone else to modify a report.
While BI tools are great for showing what is already defined and standardized. Excel is where many end up going when the question is still being defined and the answer requires flexible logic, judgment calls, and rapid iteration. That is why, even with dashboards, sales workflows often end in Excel.
Stitching together spreadsheets gets expensive
The operational issue is not Excel itself. The issue is the manual work that often surrounds it: exporting from BI tools, stitching data from CRM, billing, and other systems, cleaning fields, reconciling discrepancies, and rebuilding the same logic each reporting cycle. ThoughtSpot research release reported that spreadsheet users spend an average of 20 hours per month in spreadsheets, and that 70% of business users are not proficient in spreadsheets. That translates six full workweeks buried in spreadsheets, representing a material time allocation for many teams.
Sales teams also face a broader productivity constraint: time spent on administrative tasks reduces time available for customer-facing work. Salesforce State of Sales Report says that reps spend only about 30% of their time selling, the rest of the time figuring out what to tackle next. Meanwhile, McKinsey's "On the Social Economy" has estimated that knowledge workers can spend roughly 20% of their time searching for and gathering information, which reconciling data across tools before analysis can begin. In addition, Gartner’s press release reported in 2023 that Sales Operations teams dedicate a large portion of their time to non-sales functions, and that this share has increased substantially since 2019. In leaner companies, that workload often lands on sales leaders and managers directly.
What we're seeing across the board is that spreadsheet time is substantial. Information gathering and operational overhead consistently take a meaningful share of the workweek across roles. In many organizations, that translates into repeated manual effort around the same recurring reporting workflows.
Accuracy and trust constraints in spreadsheet-driven models
Time is not the only concern. Complex spreadsheets can be difficult to audit, and errors are common enough that they are widely studied. Research from Panko paper on spreadsheet errors reviewing real-world spreadsheet audits and error rates has found meaningful prevalence of errors in operational spreadsheets.
The implication for sales forecasting and reporting is practical: if a critical model is rebuilt frequently, edited under time pressure, and maintained across multiple versions, it becomes harder to ensure consistency and harder to explain results confidently. This is one reason why some sales teams maintain parallel systems: dashboards for consistent metrics and spreadsheets for decision logic. The goal of modernization is not to remove spreadsheets, but to reduce the manual stitching and repeat rebuild cycles that create risk and consume leadership time.
A practical modernization approach for sales analytics
Modernizing sales analytics usually works best when it starts with a small number of high-impact workflows. The most useful step is identifying the spreadsheet-driven processes that directly influence revenue actions, then making the underlying logic reusable and connected to live data sources.
In practical terms, that often means moving from ad hoc exports and manual joins to a workflow where the sources are connected once, the definitions are documented, and the output refreshes on a schedule. Excel can still be used for exploration and iteration, but recurring reporting does not require rebuilding the same model each cycle.
What to do next?
A practical starting point is one workflow: one weekly pipeline review pack, one forecasting model, or one board reporting file. Nockpoint connects the underlying tools, preserves the business logic sales leaders already rely on, and produces the output on a schedule so the same questions do not require repeated exports and manual reconstruction. Start by booking a 20-minute workflow assessment and receive a plan for converting one spreadsheet-driven reporting process into an automated, repeatable model. Book a workflow assessment
TL;DR
Excel is still where many sales leaders do the real work, even when they have a business intelligence tool, because forecasting, gap analysis, and board reporting require fast, flexible decision logic.
The problem is not Excel. The problem is the manual stitching around it: exporting, joining data across CRM and billing tools, reconciling definitions, and rebuilding the same models every cycle. Research suggests spreadsheets consume substantial time for many business users and can introduce accuracy risk when complex models are rebuilt frequently.
A practical modernization approach is to keep Excel’s flexibility while making recurring spreadsheet workflows reusable and refreshable on live connected data. Nockpoint’s approach is to connect the underlying tools, preserve the business logic, and automate the recurring outputs so sales leaders can spend more time on strategy and coaching instead of spreadsheet assembly.
