The Evolving Importance of Revenue in Startups

Historically, the term “revenue” was often avoided in discussions within the startup ecosystem. Thankfully, this perspective has shifted, particularly with the growth of Software as a Service (SaaS) businesses and the emergence of new funding models like revenue-based investing from venture capital firms.

Despite this change, constructing accurate revenue models continues to present a significant hurdle for founders. Predicting future income streams is inherently difficult when the business itself is still under development.

Achieving Predictable Revenue Streams

Successfully forecasting revenue requires a two-pronged approach. First, businesses must establish revenue streams that are inherently predictable, repeatable, and scalable. Secondly, leveraging data-driven tools is crucial for building reliable projections.

Below, we outline strategies to enhance revenue visibility, identify key data points, and implement a structured process for forecasting future earnings.

Gaining Revenue Visibility and Identifying Key Data

• Focus on understanding the core components that drive your income.
• Determine which metrics are most indicative of future performance.
• Establish a system for consistently tracking these vital data points.

By prioritizing these steps, founders can move beyond guesswork and towards more informed and accurate revenue forecasts.

Creating a robust forecasting process allows for better financial planning and ultimately, increased chances of success.

Building Forecasts on Consistent and Expandable Outcomes

Aaron Ross, a co-author of the influential work “Predictable Revenue,” formulated a methodology and assembled a team that significantly contributed to Salesforce’s revenue increase – exceeding $100 million. The concept of predictability is central to his approach. He emphasizes the need for growth that isn’t reliant on speculation, wishful thinking, or desperate attempts to close deals at the end of each quarter and year.

This principle makes revenue generated through recurring models especially attractive, although it isn’t the sole determinant of predictable income. While recurring revenue streams aren’t without risk – customer attrition and the eventual slowing of organic expansion are potential concerns – a wider perspective on predictable revenue extends beyond purely subscription-based approaches.

Ross, alongside his co-author Marylou Tyler, identifies three essential components for achieving predictable revenue: reliable lead generation, a specialized sales development team, and standardized sales procedures. Their comprehensive exploration of this topic is detailed in their book. The core message is this: financial projections should be grounded in processes and outcomes that demonstrate both repeatability and scalability.

Key Elements of Predictable Revenue

• Predictable Lead Generation: Establishing consistent sources of qualified leads.
• Dedicated Sales Development: Employing a focused team to nurture and qualify leads.
• Consistent Sales Systems: Implementing standardized processes for efficient sales execution.

Ultimately, basing projections on unstable or limited results can lead to inaccurate forecasts. A robust and reliable system is crucial for sustainable growth.

Navigating the "Hot Coals" Phase for Startups

Startups often experience initial growth through organic means like word-of-mouth, fortunate circumstances, and dedicated effort. However, this initial momentum frequently proves unsustainable in the long term. As commonly stated, the strategies that facilitate early success are unlikely to drive continued advancement.

A period of ambiguity and inconsistent outcomes typically follows, a stage Ross aptly terms “the hot coals.” This represents a critical juncture for many emerging companies.

Prior to encountering the "hot coals," accurate revenue forecasting is often impractical, if not entirely impossible. Many founders can relate to the difficulty of providing long-term financial projections when a stable revenue model hasn't yet been established.

One founder recalls the challenge of responding to a traditional investor’s request for five-year profit-and-loss forecasts when their startup was still searching for a reliable path to profitability. While not all seed investors demand such detailed projections, the underlying issue remains consistent for most entrepreneurs.

The core question becomes: how can founders reconcile the expectations of conventional financial planning with the unpredictable nature of a startup’s early stages?

Data provides a crucial solution, and even in the earliest phases, startups generate valuable data points. A previous analysis of revenue-based investing identified several key sources for revenue visibility.

According to venture capitalist Ali Hamed, these sources include:

Data Consolidation Strategies

Analyzing data residing on external platforms begins with importing it into your central data warehouse. Several tools simplify this process for those preferring a self-service approach, including Fivetran, Segment, and Stitch, all offering pre-configured connectors that minimize the need for extensive coding expertise.

As an example, Stitch provides integrations capable of extracting data from platforms like Recurly, Shopify, Square, Stripe, and Zuora, and then loading it into your preferred data storage solution – be it a data warehouse or a data lake.

Leveraging SQL for Data Modeling

Subsequent data manipulation is effectively achieved using SQL. If the objective is to model subscription revenue, a comprehensive monthly recurring revenue (MRR) playbook, developed and openly shared on GitHub by Fishtown Analytics (creators of dbt), can be utilized.

A corresponding blog post provides guidance on implementing this playbook, specifically targeting data analysts within SaaS companies or e-commerce businesses that incorporate subscription models, who will need to assess key metrics such as customer churn, upgrades, and downgrades.

Forecasting with Existing Tools

After MRR data has been thoroughly analyzed, it can be seamlessly integrated into a variety of publicly accessible forecasting applications. Options include tools provided by Chargebee, Baremetrics, and Zoho.

Alternatives to Manual Financial Modeling

It's worth noting that platforms like Chargebee and Baremetrics offer comprehensive solutions, potentially eliminating the need for the extract, load, and transform (ELT) data procedures detailed here.

Furthermore, numerous commercial tools and services specialize in forecasting; conversely, you could opt to perform your own projections utilizing software like Excel.

Limitations of Spreadsheet-Based Forecasting

As companies scale, financial forecasting methods overly dependent on spreadsheets and manual adjustments reveal inherent limitations.

This is where financial planning and analysis (FP&A) tools, including Jirav and Planful, become invaluable.

Recently, Abacum, a YC W21 graduate, has entered the market, prioritizing collaborative features and serving clients such as Jeff, Seedtag, and Typeform.

The Value of Predictable Revenue

Regardless of the chosen approach, achieving predictable revenue provides significant advantages and expands strategic possibilities.

This predictability empowers businesses, whether they pursue bootstrapping or external funding.