Over the last decade, the software stack of companies has changed dramatically in many parts of the organization. We witnessed the rise of data-enabled software for sales, people, and marketing teams. Gong.io and Chorus use machine learning to train sales reps in real time, Lattice generates and leverages data to make management more effective, and marketers use Segment to understand customer behavior. Although each of these tools is different in nature, they are similar in objective – leverage data to make their users more effective. They also pulled countless workflows out of old school, generic spreadsheets into purpose built software. There is one part of the organization, however, that has been left behind in this transformation – finance.
Conversations with finance teams have painted a bleak picture of the status quo today, even for some of the most sophisticated organizations. The primary software options for powering financial operations can be inefficient and cumbersome, like Excel spreadsheets and legacy ERP software solutions. Companies are spending large amounts on these tools – IDC estimates the global performance management and analytic applications market to reach $21B by 2021. There is also a large market for consulting services related to financial forecasting software implementations, which often leaves behind frustrated teams with limited ability to customize their new solutions. Finance teams manually input performance data after collecting it from each department using messy CSVs and surveys. After data is uploaded, it cycles through review cycles with leadership who want to debate performance projections and assumptions before the end of the quarter. At the end of it all, finance teams have spent the bulk of their time collecting, uploading, and finalizing numbers, rather than where they can truly add value – providing strategic recommendations.
However, the story has started to look different for a small number of forward thinking companies. Finance teams, especially those with digitally native employees and managers, are acutely aware of the outsized role that data plays in their work. One of the most impressive examples of this we saw was a finance team that hired a separate BI team to leverage Tableau to aggregate and analyze real-time data that feeds into their financial projections. One of our portfolio companies is similarly leveraging BigQuery to feed assumptions into their financial forecast. Other earlier stage companies have described their ideal financial stack being rooted in BI tools like Metabase and feeding into Excel, eliminating reliance on other software providers completely. We believe these cases are not exceptions to the rule, but rather early examples of what next-generation finance looks like.
Why Now? The Perfect Intersection of Two Major Trends
Data availability and data mobility have led to a tremendous transformation in data’s utility throughout an enterprise in the last several years.
- Data Availability. The first and most obvious shift was the movement of other teams in the organization to the cloud, as mentioned earlier. As more workflows shifted into software, more data was created about the state of performance of people and processes. SaaS apps have become treasure troves of company performance data that is crucial to financial forecasting and modeling. Headcount and salaries, marketing performance data, and product roadmaps are all documented in software. When Excel and Netsuite were created, this was not true and data was much more siloed across the organization, oftentimes on-premise.
- Data Mobility. The second shift, which has dramatically accelerated in the last 2-3 years, is the rapidly increasing mobility of data. There is an API within and between almost every piece of software now, especially in finance. Payroll APIs like Finch, Pinwheel, and Argyle entered the market in 2020, alongside accounting data APIs like Codat and Railz. This is only supplementing the APIs that now are often native to SaaS apps. In cases where APIs are not available, companies like Flatfile.io have enabled easier ingestion of raw CSV files to fill the gap. Increased data mobility will make it easier for finance teams to leverage operational data in financial forecasting tools.
Today, data has become so widely available and accessible that it is a core part of the way we work and make decisions. Relevant to financial data, we have seen a rapid rise in data-driven lending. Alternative financing is a necessity for many businesses, and now more than ever lenders are looking at financial data to underwrite. We have seen a surge in new capital options for businesses in recent years, such as A/R financing and revenue share based loans. Some of the newer entrants to the space, such as Pipe and Capchase, leverage data even more than incumbents. We believe a financial forecast is the key to capital, and the need for an accurate, real-time, data-rich forecast has never been greater.
The Shortfalls of Previous Generations
Three waves of financial forecasting software preceded the wave that is emerging today. The first wave was created by a single player – Excel. Although not outfitted for financial forecasting specifically, it quickly became the standard for all modeling in finance. It is simple enough to start building with limited knowledge, but also supports complex calculations and data manipulations that no other tools had before. It is the skill that new entrants to the workforce pride themselves on. However, it’s now clear that this is not the ideal solution for highly collaborative workflows like financial planning and that it does not scale well with an organization as they grow in size and complexity.
After Excel, the first wave of cloud software providers like Oracle and SAP entered the scene. As owners of other ERP solutions, there was a natural opportunity to also own financial planning and pull data from other parts of their software into financial models. These solutions are a stark contrast to the simplicity of Excel – they can be expensive, difficult to configure, and challenging to learn to use. One of the limiting features of these solutions is their “closed system” approach and lack of interoperability – it is easy to integrate data from other tools in their software suite, but not from any external software providers.
In recent years, a new set of companies saw this opportunity to make an easier to use tool, that is natively cloud based and can easily ingest data from other tools. Companies in this bucket include Hyperion, Adaptive Insights, Anaplan, Vena, Planful, and others. It would be unfair to write off all of these players. Adaptive Insights was acquired by Workday in 2018 for $1.5B, and Anaplan went public in 2018 and now has a $12B market cap. These outcomes are both great success stories and validation of the market opportunity. However, these platforms are still not the best in terms of usability and ease of configuration. Other players that entered the market around the same time stumbled for product market fit in other ways. They often faced fierce competition and didn’t match the robustness coming from rival legacy solutions. They also received pushback when trying to pull executives off of the tool they were most comfortable with – Excel – for forecasting and data uploads.
Opportunities for the Next Generation of Financial Software
We believe there are two massive market opportunities for the incoming wave of financial software providers. These two opportunities also serve as great axis to think about the market landscape.
The first opportunity is to create a tool that is highly accessible for SMB and mid-market enterprises. All of the incumbents mentioned above have ignored this segment to focus on larger companies with more complex use cases. We believe there is an opportunity to build an easy to use product for small businesses and build features alongside the biggest champions as they grow. In many cases, these companies are also already users of products that support APIs and easier data mobility.
The second opportunity is to embrace BI tools and data warehouses as the system of record for financial forecasting data. This opportunity and the first are not mutually exclusive – we believe this opportunity exists across SMBs, mid-market, and enterprise accounts. The problem with simply pulling data from other apps directly into financial forecasting tools is that the data is not analyzed and structured in a way that is ideal to feed directly into a model. What is more likely is that companies will take advantage of advancements in data mobility to first send data to a data warehouse (ie. Metabase, BigQuery) to define and generate the metrics that matter most to their business. These metrics become increasingly unique as companies grow. Then from there, it will be fed into their financial forecasting software that sits on top. The implication here is that BI teams and finance teams will work more closely together and blend together to create new roles. Five years from now, SQL and Python will be listed in the same line as Excel as a required skill set for an increasing number of roles in finance.
Introducing the Next Generation of Financial Software
In the last year, we have seen the emergence of over a dozen startups tackling one or both of the opportunities above. Some companies on our radar are displayed above. We expect to see many more in the coming months and years. While the opportunities outlined above provide two very general characteristics we are looking for, there are a handful of other characteristics that we think will define the next generation of financial forecasting software.
- Collaboration First – Finance is one of the most collaborative, data-rich pockets of an enterprise. A finance team’s primary function – reporting and forecasting – requires data contributions from every part of the organization to understand plans and budget requests, and create underlying model assumptions. Today, this process is largely manual and happens over emails, ad hoc forms, spreadsheets, and unnecessary meetings. The larger the organization, the more important collaboration becomes. The next generation of financial forecasting software will host collaboration natively, just as Figma and Jira do for designers and engineers.
- Data integrations with other enterprise software – New tools will connect into an entire ecosystem of other software tools, rather than requiring that all data is natively created in their system. These integrations will empower financial teams to leverage more data, and have more up-to-date models. We think companies will make purchasing decisions based on supported integrations.
- Easy Customization and Configuration – Finance teams are tired of being heavily reliant on other people. This problem needs to be addressed in two dimensions – onboarding and ongoing usage. The next generation of financial products will not require consultants to configure and edit models like existing legacy products do. New tools will also need to make it simple for other teams (ie. sales, HR) to send data that is used to generate models to finance teams. If finance starts to use more sophisticated data warehouse solutions that are difficult to use without a SQL background, this will become especially important. There is an opportunity to create a tool that makes the creation and collection of new metrics for a model simpler, reducing reliance on engineering, product, and data teams.
- Delivery of Novel Insights – The next generation of financial forecasting tools will do more than just enable the creation of a financial model. There is an opportunity to provide not just more features (i.e., collaboration and customization), but more data. Integrating benchmark metrics from peers and other insights into the planning process can be extremely valuable, especially for SMBs with limited historical data. For example, understanding if you should negotiate different prices with specific vendors, how performance has changed in a certain part of the organization, and what assumptions might be likely to change due to macro conditions.
- Business Model Innovation – Historically, financial forecasting tools have been seen as table stakes, but not drivers of the bottom line. While the next generation of financial software will change this paradigm, we expect them to embrace alternative business models that better align value and cost in the near term. For example, pricing based on size of the org, charging for additional insights models, or data integrations. We believe this can result in a win for both the company and their customers.
As investors and in some cases former entrepreneurs ourselves, our team at Two Sigma Ventures is excited about the potential for the next generation of financial forecasting tools to empower and enable not just financial teams, but companies themselves. If you are thinking about or building in this space, please reach out. We would love to chat!