The problem of relying solely on Excel for investment analysis.

Our team has analyzed thousands of companies over the years. To streamline this process, we initially created a detailed Excel template that automated many of the core financial analyses. However, as we scaled, several limitations became evident—Excel simply couldn’t keep up with the growing complexity and volume of data we were working with.

Sure, Excel is an incredibly powerful system, and at Intrinsiq, we don’t aim to replace it entirely. Instead, we pick up where Excel leaves off, automating the most complex workflows while allowing Excel to shine as a flexible, ad-hoc analysis tool. This way, Investment and Finance teams can leverage the best of both worlds.

1. Excel Lacks Database Functionality

While Excel works well for small-scale data analysis, it struggles when used as a database. Without a centralized system, data like P&Ls, customer cubes, balance sheets, and pitch decks were manually entered and saved across multiple files or formats. This fragmented approach made it difficult to access all the relevant information in one go, limiting broader trend analysis.

Example: When analyzing a new company, we often found key data points spread across multiple Excel files, PDFs, and pitch decks. Consolidating this data into one place wasn’t just time-consuming—it often resulted in critical details being overlooked, delaying decision-making.

2. Difficulty Aggregating Data Across Companies

As the number of companies we analyzed grew, aggregating data across multiple entities in Excel became challenging. Our files grew too large, resulting in slow performance and the inability to draw meaningful comparisons. Without proper aggregation, we were missing key market trends that could only be derived from analyzing a broader data set.

Example: Aggregating financial data from multiple companies became cumbersome as Excel struggled to handle large datasets. For example, tracking revenue growth across multiple SaaS firms or compiling customer metrics for analysis often led to performance issues in Excel, slowing down critical assessments.

3. Inconsistent Metric Calculation Across Teams

Different team members calculated metrics in Excel in slightly different ways, which led to discrepancies in the results. Without standardized formulas, we found it hard to ensure that metrics like Lifetime Value (LTV) or CAC Payback were being calculated uniformly across all companies. This made it difficult to conduct reliable cross-company comparisons.

Example: One team member might include only direct sales costs in their CAC Payback calculation, while another might factor in marketing overhead. These inconsistencies across analysts made it impossible to compare companies accurately, leading to unreliable insights.

4. Manual Data Entry and Lack of Automation

Many aspects of our financial modeling required manual data entry, which was not only time-consuming but also prone to human error. A significant portion of this data came from Pitch Decks (PDFs) or other non-structured sources, requiring us to manually extract and input financial information into Excel. Similarly, when dealing with customer-level revenue data (customer cubes), the data often had to be cleaned or restructured before any analysis could take place.

Example: We often received customer cubes with unstructured data, which meant our analysts had to spend hours cleaning, sorting, and inputting the data into Excel before any analysis could start. This bottleneck delayed our ability to make timely decisions on whether to pursue certain investment opportunities.

5. Limited Ability to Analyze Cohorts or Segments

Excel struggled when it came to filtering and segmenting data. For example, if we wanted to analyze only SaaS companies in a specific region or look at customer cohorts by revenue, Excel lacked the real-time filtering and segmenting capabilities needed for deeper analysis.

Example: To identify key trends, such as growth rates for SaaS companies operating in the healthcare sector in Europe, we often needed to manually filter through large datasets. This process was slow and prone to errors, making Excel a less effective tool for these types of segmented analyses.

How Intrinsiq Solves These Limitations

We built Intrinsiq to address these limitations. Our platform provides the database functionality that Excel lacks, enabling users to store, organize, and search through large datasets effortlessly.

It also allows for real-time filtering and segmentation, making it easy to analyze specific subsets of companies by business model, region, or sector. By automating the calculation of over 6,000 standardized KPIs, Intrinsiq ensures that metrics are consistent across all analyses, eliminating the discrepancies we encountered in Excel.

In addition, our platform fully automates data entry and standardization, saving countless hours and ensuring that the data used for analysis is accurate and reliable.

Conclusion: Moving Beyond Excel for Scalable Investment Analysis

Excel is and will always be a valuable tool, but as the complexity of our analyses grew, we realized we needed a more advanced solution to complement it, one that could handle the scale and intricacies of modern data without sacrificing flexibility. With Intrinsiq, we’ve built a platform that overcomes Excel’s limitations, offering powerful cohort analysis, data aggregation, and real-time insights that empower investment and finance teams to make more informed decisions faster and with greater accuracy.

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