This section goes out to all our fellow data nerds … plus, anyone who wants to better understand the charts and graphs contained within the reports.
To pull together the full story, we rely on ~300 ecommerce businesses, the experience of our analysts, and a pinch of CTC data magic.
To be included in Ecommerce Data, stores must meet three requirements …
Report revenue OR Facebook Spend OR Google Ads Spend for all but 4 days between January 1, 2020 and the end of the most-recent week
Be consistently reporting and connected to Statlas for at least the last month; stores must be eligible for our dataset for 1 month prior to being added to that dataset
Be included in the list of currencies we can convert:
Here’s a rundown of the formulas we use to calculate the Ecommerce Data …
Total Ecommerce Revenue ÷ Total Stores reporting
Total Channel Spend ÷ Total Stores reporting
(Total Channel Spend x 1000) ÷ Total Channel Impressions
Total Channel Revenue ÷ Total Channel Spend
Total Spend ÷ Total Clicks
Total Channel Clicks ÷ Total Channel Impressions
Total Channel Buys ÷ Total Channel Clicks
We employ three parameters before calculating:
(Σ All Stores(Total Store Revenue with Spend ÷ Total Store Ad Spend)) ÷ Number of Stores
Total Revenue ÷ Total Orders
Total Ad Spend ÷ Total Customers
Total Ad Spend ÷ Total New Customers
Total New Customer Revenue ÷ Total Ad Spend
We’ve covered a lot of data points, formulas, and metrics in this methodology. But, ultimately, our goal is to help you understand how these metrics play into the success of an overall campaign.
The best way to do that?
From a high level, the ROAS formula looks like this:
ROAS = Revenue ÷ Spend
By decomposing the equation, you can see how different parts of the funnel are performing by breaking down “revenue” and “spend” into some of the metrics we previously mentioned.
ROAS = (1 ÷ CPC) x CR x AOV
The equation can be decomposed even further, pulling in CPM and CTR:
ROAS = (1000 ÷ CPM) x CTR x CR x AOV
So, why should you care about all of these metrics?
Because every single one of them contributes to the success of an ad campaign. And, understanding what each metric means — and its importance within the overall health of your business — enables you to pinpoint the exact areas of improvement on a granular (and more tangible) level.
If necessary, we make two adjustments so as not to exclude data that is over-attributed. Meaning, the platform has attributed more revenue than the store actually received on a given day —i.e., if Facebook attributed $1,000 to a store, but the store only reported $950, then we adjust the revenue down to $950.
Channel revenue on a day must be equal to or less than all of the reported revenue from the actual store; each individual channel’s revenue is adjusted down where necessary proportional to the revenue claimed by that individual channel divided by the revenue claimed by all channels:
channel revenue = (Revenue claimed by individual Channel ÷ Revenue claimed by all Channels) * Total Store Revenue
Channel purchases claimed by all channels for a store cannot exceed the total number of purchases from that store on any given day. If purchases claimed by all channels exceed the number of total store purchases, the individual channel purchases are adjusted down proportionately to the claimed purchases:
channel purchases = (Purchases claimed by individual Channel ÷ Purchases claimed by all Channels) * Total Store Purchases