Valuation multiples of e-commerce companies will range greatly depending on the size of the company and the products/services that the e-commerce company sells.
For example, Amazon is an e-commerce company and so is a local ma-and-pop shop that sells handmade vegan candles who outsources a web designer to create a website for them to sell their candles across the country.
So, you can’t rely too much on valuation multiples of a subset of e-commerce companies unless they are similar in size and the type of products and logistics.
That said, looking at publicly available data, the average valuation multiples for e-commerce companies is ~2.5x for revenue multiples and ~20x for EBITDA multiples. Here’s why (below).
Valuation Multiples for Public Comparable E-commerce Companies
There are many e-commerce companies ranging from small to gigantic, but not many of them are public.
As a result, public comparable companies’ data to find out the average valuation multiples is very limited.
But with what I could find on Capital IQ (paid database) and scrubbing out mega large companies, negative numbers, and outliers, we have 7 companies in the data set.
From this very small data set, the average/median revenue multiple range is 2.2x to 2.3x and the average/median EBITDA multiple range is ~20x to ~32x.
I don’t find this data set to be reliable since it is so limited.
So, let’s look at e-commerce transactions and acquisitions to see if the average valuation multiples are in a similar range to the small dataset of public company comparables.
Valuation Multiples for E-commerce Companies Transactions
With e-commerce companies’ transactions, you’ll see a lot of activity, but they are mostly private companies.
For example, just running a simple search on Crunchbase shows that there is a lot of activity in the private e-commerce company transactions space like the screenshot:
But again, they are private company transactions, so they don’t disclose financial data.
We turn again to Capital IQ to see what public transactions there have been.
The transactions list screened for e-commerce company acquisitions between January 2012 and July 2021.
Transactions with no financial data available were removed from the dataset and so were outliers.
Below is the result of the M&A transactions search:
From this data set, you can see that the average/median revenue multiple range is 2.7x to 3.1x and the average/median EBITDA multiple range is 19.3x to 19.9x.
Putting It Altogether – Summary of Valuation Multiples for E-Commerce Companies
The public companies’ comparable dataset was limited as you saw above. So, I wouldn’t solely rely on the public companies for the valuation multiples for e-commerce companies.
We should look at the M&A transactions of e-commerce companies in tandem.
And from the M&A transactions, you can see that the ranges for revenue multiples and EBITDA multiples are pretty close in range to the public companies. That gives me more peace of mind.
So, all in all when you put together both the public companies data set and the M&A transactions for e-commerce companies, I feel comfortable with the below summary:
- Revenue Multiple: 2.5x
- Why? Because the public companies average/median revenue multiple is ~2.2x and the M&A transactions average/median revenue multiple is ~2.9x.
- EBITDA Multiple: 20x
- Why? Because the public companies average/median EBITDA multiple is ~26x while the M&A transactions average/median EBITDA multiple is ~19.6x. There is more data available with the M&A transactions and the average and median are clustered around 20x.
Finally, as a sense check, I looked up the average EBITDA multiple for a similar industry on Professor Damodaran’s NYU Stern webpage. (I love Professor Damodaran’s work – my review of his book “The Little Book of Valuation” can be found here if you’re interested.)
Although there isn’t a specific industry for e-commerce companies, the closest industry data is “Retail – Online”, and the average EBITDA multiple for this industry is 19.06, which is really close to our average EBITDA multiple of 20x. (This makes me feel comfortable that our data analysis isn’t wildly off.)
Hope this helps and as usual, leave me a comment with other industries you’d like to see an analysis on and I’ll try my best to write that post soon.