- Chew On This
- How We Analyze Our Marketing Data
How We Analyze Our Marketing Data
Get our exact metrics & thought process for analyzing marketing performance on a daily, weekly, and monthly basis.
Welcome back for some more bites to chew on.
It’s time for some gentle metrics madness.
This week, we’re focused on marketing metrics (powered by Triple Whale 🐋🐋🐋, BTW) and covering what we analyze on a daily, weekly, and monthly basis.
First Course: Our Daily Metrics & Thought Process
Day-to-day, we’re typically looking at the following metrics:
New Customer ROAS
How we think through this:
Contribution margin as the success barometer: If we’re seeing positive contribution margin over the course of the day, that’s ideal.
But does that mean we can spend a little bit more on marketing? If we have a buffer, we’ll go back into the account and increase our spend.
If our contribution margin is negative, it means we’re losing money on every customer acquisition.
So, we ask:
How deep in the red are we that day? If it's a couple of bucks per order, we’ll probably leave spending alone. But if we're losing $10 in order, then we’ll probably go into the ad account and see where the issues are: we might pull bids or budgets back to see if we can level out.
We’re also looking at the difference in returning customer revenue vs. new customer revenue, because returning revenue is going to be a lot more profitable than new customer revenue.
So if we have a day where there's a lot of returning customers coming in, maybe it's a sale or product launch, we can support losing money on the front end—we’ll let things rock and roll.
Costumes are optional.
We know we’ll make the money back over time, so we can afford the first-purchase loss.
Then: At the end of the day, we’re looking at net profit.
How did we do day over day?
Did we keep more or less than we expected?
🌟 When looking at intraday metrics, we’re usually keeping an eye on things hourly and watching out for spikes and drops. And we’re asking ourselves: where did they come from? If it’s a negative move, we might immediately check if there’s something wrong with the website, or a main ad platform, like Facebook. However, we try not to make any real changes to spend unless things change dramatically.
🎵 Notes on weekly context: You absolutely need to know what your better and worse days are and how your ad accounts react. For Obvi, Monday and Tuesday are our slowest days. So, typically, Sunday evening going into Monday, we’ll pull back spend just to account for that.
In doing so, we’re able to limit loss and maybe break even. Then, on Wednesday morning and going into the weekend, typically things will get better. We’ll start to bring bids up at that point.
Second Course: Our Weekly Metrics & Analysis
On a weekly basis, we try to look at the same metrics, but compare week-over-week time periods rather than day-over-day periods.
Are we doing better or worse than we should be? If we’re still in the green, great. But not every week is in the green.
What are some of the bigger issues that we need to take care of? Everyone has off weeks. So, we’re asking:
What are next week’s creatives going to look like?
What were the worst-performing creatives from last week?
What are some of the tests that we're running on our landing pages, and how are they doing?
Do we need to start thinking about a different offer?
Is it general seasonality?
Are we going into a holiday or the holiday season?
🌟We’re keeping track of our performance in the context of generally knowing how our brand performs. If we’re doing way worse than expected, we’re going to be looking in detail at particular channels and platforms. Something may have broken, there may be something wrong with one of our accounts, and we may need to contact a rep.
Third Course: Our Monthly Review
On a monthly basis, we're looking at high level performance and asking a long list of questions:
Overall, are we green or red?
What was our total net profit, when all is said and done?
What was our net profit on a channel basis?
What were our most profitable (%) channels?
What was really driving our profitability?
When and where did we shine?
How did our affiliates/influencers perform overall?
Who were our best- and worst-performers?
How many affiliates/influencers were posting about us?
Can we double down on what worked next month?
Are we just spending too much/too little on marketing?
Is our overall recurring revenue strong?
Are we maintaining a strong repurchase rate?
We’ll also drill down into ads for a more detailed analysis:
What were our best and worst ads?
Why did some perform better than others?
What ads are getting ad spend?
Which ads are actually getting pushed by Meta?
Is our cost of traffic in line with where it usually is?
How are our clickthrough rates?
Are our ads engaging enough?
Then, we’ll look at additional shopping funnel metrics:
Cost per add to cart
Cost per initiate checkout
Cost per purchase
Effectively, we don’t want to miss any part of our performance, and we never want to miss an area to improve month-over-month.
Tool of the Week
Speaking of data…
Elevar gives you 99% tracking and conversion data accuracy.
(That could be the end of this section.)
Elevar is the ultimate set-it-and-forget-it tool, and its server-side tracking tech enables brands to overcome the extended 💩storm of Apple updates and the rise of GA4 (we’re still pouring one out for Universal Analytics).
And, although Shopify offers an integration with Meta to help data accuracy, it’s just not as good. We’ve tested it and other solutions.
Elevar is the only tool that gets you to nearly perfect accuracy.
Plus, with Elevar:
Track each customer journey
Increase site speed (reduce the # of other scripts running)
Compare all event data by channel
Leverage 50+ integrations
It’s pretty simple, really. Stay in data darkness, or use Elevar.
We sincerely appreciate every moment you spend with us and reading our work. We’ll see you soon.
All the best,
Ron & Ash