In this Revenue Models lesson, you'll learn how to build a revenue model for a consumer retail company.
By http://breakingintowallstreet.com/ "Financial Modeling Training And Career Resources For Aspiring Investment Bankers"
Chuck E. Cheese, a kids' restaurant chain that was acquired by Apollo for $1.3 billion, is used in this example since their data is readily available and easy to use
Table of Contents:
0:39 Why Revenue Models Are Important
2:19 How to Set Up Revenue Models - Units Sold and Market Size Methods
3:39 How You Build a Revenue Model - Examples for Different Industries
5:03 Step 1 - Finding Historical Data
5:59 Step 2 - Assumptions for Stores Opened and Closed
8:02 Step 3 - Assumptions for Sales per Store Growth
9:03 Step 4 - Calculating Ending Stores per Year
10:30 Step 5 - Toggle Calculations for Sales per Store
11:08 Step 6 - Splitting Revenue Into Segments
14:20 Step 7 - How to Review and Tweak the Numbers
15:18 Recap and Summary
Why Do Revenue Models Matter?
It's a very common topic in case studies and interviews in IB, PE, HFs, and anything else in finance.
Revenue models can come up in LBO case studies, 3-statement modeling case studies, normal interview questions, and, of course, on the job.
Often, you have enough data to make MORE than just a simple % growth rate assumption for revenue... but not enough data to do the same on the expense side.
Theoretically, you could just say 2%, 3%, 4%, etc. growth each year and project revenue like that.
BUT it's much more credible to say, "We have 50 stores each generating $2 million in annual sales, on average, and we plan to open 5 new stores per year for the next 5 years -- based on that, revenue is expected to be..." rather than "We're assuming 4% revenue growth per year."
The numbers you get will NOT necessarily be different or "more accurate" -- you're still predicting the future!
But at least your numbers will have more real-world support behind them...
What is a Revenue Model?
It can be done many different ways, but most revenue models boil down to Units Sold * Average Selling Price, or Total Market Size * % Market Share.
The best method depends on the available data, the work and research you've done, and what the company discloses.
For this consumer/retail example, it makes the most sense to use a variation on Units Sold * Average Selling Price, since "market share" is almost impossible to establish for a large and fragmented market like restaurants.
How Do You Build a Revenue Model?
For retailers, you can divide revenue into into existing stores vs. new stores and assume a figure for average Sales per Square Foot/Meter, or Sales per Store, and then make assumptions for new stores opened, stores closed, and how the sales per store figures change over time.
Here's what we cover in this example for Chuck E. Cheese:
Step 1: Get the historical data you need -- in this case, the # of stores opened and closed in prior years, and the average sales per store type. These are all taken from the company's filings.
Step 2: Make assumptions for the # of stores opened and closed each year -- companies often disclose their plans in their filings, or you can extrapolate from historical data. In this case, CEC told us directly how many stores it planned to open over the next 4 years.
Step 3: Assume a growth rate in Sales per Comparable (Existing) Store, and Sales per New Store.
Step 4: Calculate Ending Stores each year, with support for the sensitivity toggles built in so that we can easily modify the assumptions.
Step 5: Now, make similar "post-toggle" calculations for Sales per New Store and Sales per Existing Store.
Step 6: Now, divide the revenue into segments, if applicable... it is very much applicable here! There are different margins for entertainment vs. food and beverages, and there's a clear trend in one direction (away from food and beverages).
Step 7: Now, go back and check your numbers, fill in the miscellaneous and smaller items, and see how equity research estimates (and other sources) compare to what you've come up with.
Go back and tweak your numbers as necessary.
Pick a company you're interested in, in an industry that's relatively easy to analyze, and project revenue based on what's in their filings.
It doesn't have to be super-complicated -- for most companies, revenue comes down to less than 5 key drivers.
Avoid conglomerates, companies with tons of business lines, or industries that are more complex, such as oil & gas, commercial banking, etc.
Suggestions: Airlines, technology, consumer/retail, industrials/manufacturing, healthcare is iffy because it can get very complex to model a company with a huge drug portfolio.