Core Revenue Drivers: Unit Economics by Industry
The unit economics used is going to be company-specific, but common examples of metrics used to calculate revenue include:
- Average Contract Value (“ACV”)
- Average Revenue Per Account (“ARPA”)
- Number of Active Accounts (or Leads in Pipeline)
- Sales Productivity (New Customers Acquired Per Rep)
- Average Contract Term
|Online B2C / D2C Businesses
- Average Order Value (“AOV”)
- Average Selling Price (“ASP”)
- Average Number of Orders Placed (and Products Per Order)
- Average Number of Orders Per Year
- Average Daily / Monthly Traffic (and % of Visitors Paying)
|E-Commerce Platforms (or Marketplace)
- Transaction Take Rate %
- Premium Monthly Fee
- Gross Merchandise Volume (“GMV”)
- Number of Active Seller and Buyer Accounts on Platform
|In-Person Stores (e.g., Retail)
- Average Revenue Per Store
- Average Order Value
- Sales Per Square Foot
- Same-Store Sales
- Number of Open Stores
- Average Number of Store Sales Representatives
- Average Number of Products Per Order
- Paying Customers % of Store Traffic
|Trucking Transportation (Freight / Distribution)
- Revenue Passenger Mile (“RPM”)
- Average Revenue Per Driver (or Truck)
- Pricing Rate Per Delivery Request
- Average Miles Driven Per Hire
- Number of Available Drivers (or Buses / Trucks)
- Average Revenue Per Kilometer (“RPK”)
- Average Revenue Per Trip
- Average Booking Fee Per Flight
- Average Miles Flown Per Month (or Year)
- Average Number of Passengers Per Flight
- Number of Licensed Planes
|Sales-Oriented Companies (e.g., Enterprise Software Sales, M&A Advisory)
- Average Deal Size (Dollar Value)
- Average Commission % Per Closed Deal
- Number of Deals Closed Per Rep
- Number of Sales Representatives
|Healthcare Sector (e.g., Hospitals, Medical Clinics)
- Average Patient Fee (Segmented by Type of Medical Procedure)
- Reimbursement Rates (e.g., Medicare, Medicaid, Managed Medicare / Medicaid, etc.)
- Treatment Costs for Uninsured Patients
- Average Length of Stay
- Average Number of Beds Per Hospital
- Average Occupancy Rate %
- Inpatient / Outpatient Mix
- Average Room Rate (and Booking Fee)
- Cancellation Fee
- Average Occupancy Rate %
- Total Number of Rooms
|Subscription-Based Companies (e.g., Streaming Networks)
- Monthly Subscription Fees (Tier-Based)
- Average Revenue Per User (“ARPU”)
- Total Active Subscriber Count
- Monthly Churn Rates (or Retention Rates)
- Returning Customers Rate %
|Social Media Networking Companies (Advertising-Based)
- Charged Rate Per Unit of Time
- Pay-Per-Click (“PPC”) Fee
- Premium Subscription Fee Per Customer
- Daily Active Users (“DAUs) or Monthly Active Users (“MAUs)
- Clicks on Ads Per Account
|Services-Based Companies (e.g., Consulting)
- Average Hourly Billing Rate
- Average Project Fee
- Average Project Duration
- Average Contracted Projects Per Year
|Financial Institutions (Traditional, Challenger / Neo Banks)
- Transaction Fee (% of TPV)
- Tier-Based Payment Fee
- Average Dollar Amount Per Lending Agreement (and Pricing Rates)
- Late Fee Structure
- Total Payment Volume (“TPV”)
- Freemium to Paying Customer Conversion %
- Number of Active Client Accounts
The process of selecting the right metrics to use is similar to that of picking the variables for a sensitivity analysis, in which the practitioner must choose relevant variables that have a material impact on the financial performance of the company (or the returns).
Bottom Up Forecasting Calculator – Excel Template
We’ll now move on to a modeling exercise, which you can access by filling out the form below.
Step 1. Revenue Forecast Model Operating Assumptions
In our example tutorial, the hypothetical scenario used in our bottoms-up forecast is of a direct-to-consumer (“D2C”) company with roughly $60mm in LTM revenue.
The D2C company sells a single product with an ASP ranging around $100-$105 in the trailing three years and a low product count per order (i.e., ~1 to 2 products each order historically).
Additionally, the D2C company is considered to be in the late-stage of its developmental lifecycle, as indicated by its sub-20% YoY revenue growth.
We begin by identifying the fundamental drivers of revenue for a standard D2C business:
Since we are given the total revenue and the total number of orders for the past three years, we can back out of the estimated average order value (AOV) by dividing the two metrics.
For instance, the AOV in 2018 was $160 and this figure grows to approximately $211 by 2020. Note that we are intentionally using the total revenue as opposed to the net revenue, as we do not want the typical order value to be skewed by refunds.
Later on, we will forecast the refund amounts separately. The inclusion of the refund amount in our formula by using net revenue would cause us to make the mistake of double-counting.
Using the provided “Average Number of Products Per Order”, we can then estimate the ASP for each year by:
Average Selling Price (ASP) = AOV ÷ Average Number of Products Per Order
The ASP of an individual product comes out to about $100 in 2018, which grows to around $105 in 2020.
Step 2. Revenue Forecasting Assumptions with Operating Cases
Now, we can create assumptions for these drivers with three different scenarios (i.e., Base Case, Upside Case, and Downside Case).
The three variables that we will project are:
- Total Number of Orders % Growth
- Number of Products Per Order % Growth
- Change in Average Selling Price (ASP)
The finished assumption section is shown below.
In practice, the assumptions used should take into account:
- Historical Growth Rates
- Comparable Companies’ Forecasts and Pricing Data
- Industry Trends (Tailwinds and Headwinds)
- Competitive Landscape
- Industry Research Reports from 3rd Party Sources
- Estimated Market Sizing (i.e., Sanity Check Assumptions)
With the historical AOVs and ASPs calculated and the forecast of the three drivers ready, we are now prepared for the next step.
Step 3. Bottom-Up Revenue Build-Up
Since we worked our way down to ASP, we will now work our way back up by starting with forecasting ASP.
Here, we will use the XLOOKUP function in Excel to grab the right growth rate based on the active case selection.
The XLOOKUP formula contains three parts, with each pertaining to three distinct scenarios:
- Active Case (e.g., Base, Upside, Downside)
- ASP Array for the 3 Cases – Finds the Line w/ the Active Case
- Array for the ASP Growth Rate – Matched to the Active Case Cell (and Outputs Value)
Therefore, the ASP growth rate for 2021 is 2.2% as the active case is switched to the base case.
Then, the prior year ASP will be multiplied by (1 + growth rate) to arrive at the current year ASP, which comes out to $107.60.
The same XLOOKUP process will be done for the number of products per order.
Note: Alternatively, we could have used the OFFSET / MATCH function.
In 2020, the average number of products per order was 2.0, and after growing by 9.1% YoY, the number of products per order is now ~2.2 in 2021.
The AOV was excluded from the revenue assumptions section, as this metric will be calculated by:
AOV = Average Number of Products Per Order × Average Selling Price (ASP)
Based on this calculation, the projected AOV in 2021 is about $235 (i.e. ASP is $107.60 and each order contains about 2.2 products on average).
To wrap up the revenue projection assumption linkages, we now grow the total number of orders using XLOOKUP again.
And finally, we can forecast total revenue by using the following formula:
Total Revenue = Total Number of Orders × Average Order Value (AOV)
Now, we have all the calculations set for the first projection year, which we can now extrapolate forward for the rest of the forecast.
Step 4. Net Revenue Calculation
Returning to refunds, which are very common and must be included in models for e-commerce and D2C companies, we simply divide the historical refund amounts by the total revenue.
The refund as a percentage of total revenue comes out to roughly 0.1%-0.2%. As this is an insignificant number, refunds will be straight-lined. The projected refund amount will be:
Refunds = Total Revenue × (Refunds % of Total Revenue)
With the refund forecast filled out, we can move on to calculating net revenue, which accounts for the refunds and avoids double-counting.
Step 5. Complete Bottom-Up Forecasting Model Analysis
The screenshot shown below is of the finished bottom-up forecasting revenue build:
From a glance, the increase in AOV is driving revenue growth, as seen from the expansion of AOV from $211 in 2020 to $298 by the end of 2025.
Upon a closer look into the same time frame, that 7.2% CAGR of AOV is being driven by the:
- Average Number of Products Per Order: 2.0 → 2.6
- Average Selling Price (ASP): $105 → $116
In closing, we can see that the net revenue of the D2C business is anticipated to grow at a 5-year CAGR of approximately 10% throughout the forecast period.
Do you have an example of this for B2B SaaS? Instead of working up from ASP, would you work up from ACV?