Most SaaS companies are aware that reporting is a challenging process, but many are not entirely sure why. Usually it comes down to the simple fact that the data is often not “reporting ready,” and this can be for a number of reasons.
One reason is the fragmentation of data. Data is often spread out across various databases, spreadsheets, and software systems. Gathering this data and consolidating it into a single report can be a complex and time-consuming task. Furthermore, data quality issues, such as inconsistent or inaccurate data, can further complicate the process of creating meaningful reports.
Another reason is the need to filter and aggregate data. With the raw data alone, there is no easy way to break it down in multiple ways for easy analysis, especially if you lack the necessary reporting capabilities and expertise to analyze and make sense of the data. This can lead to a perception that gaining insights is an expensive and resource-intensive process, requiring significant investments in tools, personnel, and infrastructure.
All of these factors can make reporting a challenging process for SaaS companies, and many struggle to identify the root causes of the difficulties they face. However, by understanding these underlying issues, you can take the necessary steps to address them and create more effective reporting processes that provide the insights you need to drive your business forward.
Here are the top four reporting challenges you’ll likely encounter when it comes to gathering and analyzing metrics:
Slow to get data
Manually creating reports can be a laborious process, especially when dealing with data that is spread across multiple sources managed by different departments. In such cases, data may be siloed or stored in disconnected systems, requiring significant effort to collect, integrate, and analyze. For example, the finance team may use one tool to manage revenue data, while the sales team uses a different tool to manage customer data, and the product team may use yet another tool to manage usage metrics.
Collecting and aggregating all of this data can be a time-consuming and error-prone task, leading to delays in reporting and potentially compromising the accuracy of the insights gained. Moreover, manually creating reports can also take up valuable time that could be better spent on higher-value activities. As a result, finance teams may have to wait several days or even weeks to receive the reports they need. Furthermore, not having fast access to data leads directly to decisions made not using data. Decisions based on intuition can be detrimental to the survival of the business.
To overcome these challenges, businesses should invest in built-in, out-of-the-box analytics and reporting that can help simplify and accelerate the reporting process, while reducing the risk of errors and improving the accuracy of the data.
High cost of ownership of a reporting and analytics engine
Many SaaS enterprises invest in a separate reporting engine, thinking that they can solve their reporting problems with a single purchase. But in reality, companies that own a separate reporting engine may be losing more money than they realize.
Not only do you have to pay for the tool itself, but there is also the significant cost of implementing the tool. Implementing a reporting engine is a complex process because it needs to seamlessly integrate with other core systems. It typically involves integrating data from multiple sources, including databases, applications, and third-party systems, and transforming this data into a format that can be used for reporting and analysis.
Many companies pay significant salaries for a third-party team to come in and build out the custom integrations, data models, reports, and manage the data warehouse. Such integrations can be complicated and time-consuming, and the costs can add up quickly. Additionally, some companies may need to buy another reporting package because the reporting in their CPQ or billing solution is almost non-existent. This can be an expensive proposition, and it can also lead to further integration challenges.
As your business grows and generates more data, the cost of storing, processing, and analyzing that data can increase significantly. Reporting and analytics engines may require additional hardware, software, or cloud resources to handle the increased data volume, adding to the cost of ownership.
Finally, the ongoing maintenance and support to keep your reporting engine running smoothly can also be a significant expense. As your business evolves and new reporting requirements arise, your reporting engine needs to be updated and adjusted to accommodate these changes. This can require ongoing development and integration efforts, which can be costly in terms of both time and resources.
Furthermore, as the reporting engine becomes more critical to your business, the cost of downtime or data errors can be significant, making it essential to have a dedicated and skilled team available to provide support and resolve issues quickly. All of these factors can contribute to the ongoing costs of maintaining and supporting a reporting engine, making it important to carefully consider the total cost of ownership.
Inconsistent and inaccurate data
One of the main reasons SaaS companies fail to obtain consistent and accurate data is due to the use of separate CPQ, billing, and revenue recognition systems. In the ideal technology stack, customer and subscription data is pulled from one reliable source of truth to provide a 360-degree view of your business performance. However, achieving this becomes a challenge when such data is scattered across disparate systems.
In addition, these systems may have different underlying algorithms and logic to perform their respective tasks. In many cases, these systems are developed by different vendors, which can further contribute to the differences in logic and functionality. This can result in inconsistencies in data interpretation, making it challenging to compare data across different systems.
For instance, the CPQ system may have a different pricing scheme compared to the billing system, leading to discrepancies in the pricing data. Additionally, the revenue recognition system may not properly account for deferred revenue or recognize revenue at different times compared to the billing system. These inconsistencies can create confusion and undermine the accuracy of your business performance metrics.
Unifying and normalizing data can result in a lack of standardization and consistency, as different teams may format subscription or customer data in varying ways. In cases where quoting systems and billing systems are siloed, updates made in one system may not reflect in the other, leading to errors that may go unnoticed until invoices are generated.
Having separate CPQ, billing, and revenue recognition systems can also lead to data duplication. For example, if customer data is entered into both the CPQ and billing systems, it can result in duplicate customer records and inconsistencies in customer data. To avoid these issues, you should consider investing in a unified quote-to-revenue system that can handle all of these functions, ensuring that data is accurate, consistent, and up-to-date. A unified system can also reduce the time and effort required for data entry and improve overall efficiency.
Lacking advanced reporting capabilities
Having a bird’s-eye view of data isn’t enough to make informed decisions about your business performance. You also need the ability to drill down into your figures and the underlying data behind them. However, building advanced reporting capabilities requires a significant investment in time and money to develop and maintain. SaaS companies typically have large volumes of data generated by multiple systems and applications. Combining data from multiple sources can be challenging because each system may have its own data format, structure, and data storage method that is not easily integrated into your existing system and tools, making it difficult to create a unified view of your business performance.
When data is stored in different systems, it can also be difficult to get a comprehensive picture of your organization’s overall health. Third-party data sources or APIs can provide limited access to all the data points you require for your analysis. These data sources may have data restrictions, access limitations, or privacy concerns that prevent them from sharing all the data they collect.
While you may be able to see how individual systems are performing, you won’t be able to see how they are interconnected or how changes in one system affect others, which can lead to a lack of granularity and details in the data.
Standalone quoting, billing, and revenue management systems may standardize their data to make it easier to consume. This may involve aggregating data or summarizing it into broader categories. While this can be useful for some types of analysis, they lack the ability to drill down on a variety of seemingly simple metrics that many CFOs and executives want to see.
For instance, looking at your ARR in one report won’t tell you enough about your revenue changes over time, so you’ll spend a lot of effort digging into the ARR Momentum for specific cohorts, product lines, or charge type. This can be particularly problematic when trying to understand the root causes of performance trends or when attempting to identify areas for improvement. Without this ability, companies may struggle to make informed decisions based on the data they have available.
Organizations facing these challenges should take the proper steps to combine data from different sources and systems into a single, unified view. In addition, your business should establish data governance policies and procedures to ensure data accuracy and consistency across all systems.
This blog post is an excerpt from our eBook "Mastering SaaS Metrics: A Guide to Measuring and Optimizing Revenue Growth." To read more, download the full guide here.