A financial model is an invaluable asset for any business leader. When properly constructed, a good financial model describes the economics of a business strategy in detail, highlighting what will need to happen now and in the future for the business to succeed. It’s a map, a sanity check – a resource that empowers startup leaders to make the decisions needed to scale their business.

A key component of a financial model is revenue modeling. Revenue modeling is a framework for forecasting a business’s revenue by examining key assumptions (such as market size, customer profile, product, and pricing) and operational constraints (such as pace of hiring, quota and sales rep ramp). Leaders use revenue models to understand how assumptions and operational data might drive growth and consider the potential impact of different strategic decisions.

It Starts With Understanding Your Business

To build an insightful, functional revenue model, we must first understand the business model and underlying levers for success. After we understand the market, product, customer, and sales strategy, we can model what growth might look like for a startup.

We then need to understand the market landscape. What is the problem we are trying to solve? Who are the competitors in the market, and how are we differentiated? Can we be differentiated? Can we disrupt? After we have a firm grasp on the market landscape, we can test assumptions (like growth rate and market share) by changing market dynamics in our revenue model to understand the potential impact.

If we know the product, we understand how value is created for the customer. Who is our ideal customer, and how do they buy? Then, we can develop an underlying marketing and go-to-market strategy. For the pricing model, we must know how the customer wants to buy. We might consider a free trial, subscription, usage, point of sale, or a combination, but the goal is to make it easy for the customer to purchase.

After we understand the market landscape and develop the ideal customer profiles for our product, we can develop the customer segments and channels through which we sell. For example, if we are an enterprise SaaS company that is selling business analytics and we know our ideal customer, then we can segment customers by deal value (small, medium, large), company type (SMB, Mid-Market, Enterprise), industry segment, and onboarding complexity (self-service, low-touch, high-touch). From there, we continue to cascade down to as much detail as needed to fully understand the business.

As we test assumptions, particularly around pricing and market acceptance, the opportunities and risks of different strategic choices become clearer, and startup leaders are empowered to make the best decision possible for their business.

Top Down and Bottom Up Revenue Modeling

A Top Down approach to revenue modeling cascades from top-level assumptions to inform decision-making. With the Top Down approach, we define key milestones, metrics (i.e., ARR, new customers), and constraints (i.e. cash runway, sales capacity), and work down into the operational details needed to support and successfully achieve the desired outcome.

If the key assumption of the business is an ARR goal and growth rate, from a Top Down approach, we must determine if the TAM (Total Addressable Market), SAM (Serviceable Addressable Market), SOM (Serviceable Obtainable Market) support the ARR goal. Then, we build the sales hiring plan and the customer segments to achieve the top-line target.

A Bottom Up approach to revenue modeling builds revenue projections based on specific operational metrics. It starts with understanding the tasks and deliverables needed to go to market and acquire customers successfully. As we build on our understanding of what different teams require, we collect this information to support our key milestones, metrics, and constraints.

A common example would be determining the amount of bookings or revenue salespeople can generate in a B2B SaaS Enterprise model. We would estimate the bookings quota for a sales team or individual salesperson, predict time for the sales rep to ramp to full quota and then plan to hire the salespeople so that the total bookings quota meets or exceeds revenue targets.

For Best Results, Use Both

The Top Down and Bottom Up approaches to revenue modeling are not mutually exclusive. By synthesizing the two into a comprehensive approach, we can increase our confidence in the revenue model we build. Let’s take a look at how Top Down and Bottom Up work together in revenue modeling.

At their most fundamental level, Top Down assumptions and metrics operate like the flat line on top of the graphic. They are not completely fixed, and they don’t have the variability that Bottom Up data does.

The Bottom Up approach builds towards the Top Down goals by testing assumptions. We build operational activities that generate revenue to achieve the Top Down goal. The line represents the effort to align with our Top Down goal.

Let’s say a fictional SaaS business is currently at $10 million ARR and, by next year, wants to reach $20 million ARR, 100% year-over-year growth. We have established our Top Down goal. To reach that goal, we need to implement operational activities from the Bottom Up.

A Top Down approach builds a model on the stated goal of getting to $20 million ARR. For example, if we know that our service offering will sell for $100k ARR on average, in order to achieve our ARR goal (growing from $10M to $20M in ARR), we will need to acquire a minimum of 100 customers and assume the percentage of existing customers that will renew or expand. In this case, the product pricing is a Top Down constraint that determines the number of customers we need to acquire. We can verify that acquiring 100 customers is a reasonable assumption by comparing it to the SOM, another Top Down constraint.

When we apply the Bottom Up approach to our example above, we might assume that one sales rep will close 10 customers per year. Under this assumption, we will need to have a productive sales team of 10 reps to meet the ARR goal. The sales reps would then build and manage the customer pipeline needed to achieve the $20 million ARR goal. From here, we could build a model around those activities and assumptions.

When used in tandem, the Top Down and Bottom Up approaches provide management teams with an operational understanding and execution requirements as they work from theory down into the detailed preparation of the financial model.

The fluctuating lines between the two vectors on the graph represent the trade-offs and iterations that occur when we apply Top Down and Bottom Up approaches and encourage strategic decision-making to balance both approaches.

By applying Top Down and Bottom Up approaches in our financial modeling, startup leaders gain critical insights into how assumptions and operational data can drive growth. This helps startup leaders understand business outcomes in alternative scenarios, ultimately leading to better decision-making, improved performance, and accountability.