What’s your go-to way to judge campaign success? Which metrics matter most? Grab this list from some website: ROAS, MER, CAC, CVR, AOV, LTV.
What attribution window/model are you using, and how do you set pass/fail thresholds? Much appreciated if someone could share tools/dashboards and examples. Thanks.
Hi @Arcs
To give you some pointers, your most important “North Star” metric should be MER (Marketing Efficiency Ratio), calculated as Total Store Revenue / Total Ad Spend. This is your true source of truth because it cuts through the inflated attribution numbers from platforms like Facebook and Google. Your primary goal is to keep your MER above your break-even point (e.g., a MER of 3.0 or higher).
Next, for long-term health, you must ensure your business model is profitable by watching your LTV to CAC ratio. The lifetime value (LTV) of a customer should be at least three times your customer acquisition cost (CAC). This confirms you are not overpaying for growth. The two daily levers you can pull to improve both MER and CAC are increasing your Conversion Rate (CVR) and, most importantly, your Average Order Value (AOV) through tactics like bundles and shipping thresholds.
For attribution, the standard is a 7-day click, 1-day view window, but always default to your MER for the real picture. While you can track this in a spreadsheet, most data-driven stores use a tool like Triple Whale or Northbeam to get a unified dashboard that calculates these key metrics automatically.
Hope this helps!
i guess as such all metrics have their own value. whenever i judge a campaign’s performance, i do it using a blend of all of them. for sure, some matter more to me than others.
for example, RoAS would always take precedence over MER as it reflects overall efficiency at the ad spend level. but apart from RoAS, i would say you should also closely track CAC, CVR, and AOV. these will enable you to understand how efficiently as a business you are acquiring customers, how well your traffic is converting, and how valuable each conversion (or purchase) is.
for attribution, a 7-day click click and 1-day view window can be good to follow. and yeah, these using a data-driven model (when available depending on the platform).
for tools, i would say depends on your specific marketing platform (google ads, facebook, etc.) for google ads, their dashboard should suffice. or google analytics can also be considered.
hi @Arcs
I usually look at ROAS, MER, and CAC first to gauge profitability, then check CVR and AOV for quick campaign health. LTV helps validate long-term return.
For attribution, I stick with a 7-day click / 1-day view window (Meta + GA4 blended view). My pass/fail rule: if MER drops below 3 or CAC rises 20% week-over-week, it’s time to tweak.
When it comes to measuring the success of a marketing campaign, I usually look at a mix of key performance metrics that reflect both efficiency and actual impact.
The first thing I check is Ad Channel performance — understanding which platforms drive the most valuable traffic helps allocate future budgets more effectively. Then comes Ad Spend, which gives me a clear picture of how much was invested versus what was achieved.
Next, I look at Units Sold and Gross Sales — these are straightforward indicators of campaign impact on revenue. But to truly understand performance, I also track Conversion Rate (CVR) and Cost per Acquisition (CPA). CVR shows how well our campaign turns viewers into buyers, while CPA tells me how efficiently we’re acquiring each customer.
Finally, Return on Ad Spend (ROAS) is the ultimate efficiency metric — it directly connects ad investment with the revenue generated.
In short, I don’t rely on just one number. A successful campaign, to me, is one that maintains strong ROAS and healthy conversion metrics while keeping acquisition costs reasonable and driving consistent sales growth.
ran a store where ROAS looked amazing but we were hemorrhaging money — turned out our return rate was killing us and none of those platform metrics accounted for it. biggest lesson i learned was to stop trusting in-platform numbers and just look at actual profit deposited in the bank account week over week. do any of you factor returns and refunds into your pass/fail thresholds?
I actually don’t even look at a campaign level success.. these days I just constantly run new campaigns and kill the ones with too high CPA. This can be after 2 days or 2 weeks… chasing the success of a specific campaign is just a lost cause.
To calculate actual MER, I first clean my data from test orders, cancelled, duplicated, returned… and all the noise, and basically keep an eye on the bottom line relative to the ad spend to make sure this ratio makes sense and is somewhat stable…
And this is only on the campaign level, there is so much more to look at for the store health.. ads are important but hardly the whole story
Spent two years trusting MER on a cross-border account before noticing the number only looked stable because consent drop was eating purchases unevenly by market. One country looked like the winner. It was just the cleanest signal, not the most profitable one. The window you pick matters less than what the platform was allowed to count before the math even starts. We moved budget for months on a denominator that was honest and a numerator that was missing a chunk.
I’d judge it more on blended business metrics than just platform ROAS.
Main ones: MER, CAC, CVR, AOV, LTV, with ROAS as a directional signal.
For pass/fail, I’d set thresholds before launch, then use AI to monitor what’s actually happening: why CAC is rising, why CVR dropped, which offer lifted AOV, and what A/B test to run next.
EnricoForte’s point is the one that sticks - the denominator can be honest while the numerator is missing a chunk. That’s exactly where most campaign measurement breaks down.
MER (total revenue / total ad spend) is a solid top-line signal, but it still assumes your revenue number is clean. Shopify’s dashboard counts revenue at order creation and doesn’t net out refunds until they’re processed. So if you’re pulling MER weekly, you’re often comparing a clean spend number against a revenue number that still includes returns you’ll absorb later.
The order of operations I’d suggest: net revenue (after refunds) minus COGS, shipping, and fees - then divide by total ad spend including anything not UTM-tracked. That’s your actual marketing efficiency. The platform ROAS number sits on top of that as a directional signal, not a source of truth.
Hi @Arcs .
Hope you are having a fantastic day!
MER and ROAS are the two tops.
Use Triple Whale or NorthBeam for attribution ( 7 days clicks and 1 day views) and also set channel-specific ROAS thresholds ( meta 2 to 4x, Google 4 to 6x).
The biggest mistake I see is focusing only on ROAS from Meta or Google. Those platform numbers are useful, but they don’t tell you how your business is performing overall.
The metric I’d treat as the “North Star” is MER (Marketing Efficiency Ratio), which is simply:
MER = Total Store Revenue ÷ Total Ad Spend
It gives you a much clearer view of whether your entire marketing investment is profitable. For many brands, knowing your break-even MER is more valuable than chasing a higher platform ROAS.
Beyond that, I’d keep an eye on:
LTV:CAC ratio – Ideally, customer lifetime value should be at least 3x your customer acquisition cost.
Conversion Rate (CVR) – Small improvements here can have a significant impact on profitability.
Average Order Value (AOV) – Bundles, upsells, and free-shipping thresholds are often the easiest ways to improve returns.
For attribution, a 7-day click / 1-day view window is still a common benchmark, but I always compare it against overall business performance instead of relying solely on ad platform reports.
If you want to understand how a Unified Marketing Measurement (UMM) Platform combines marketing mix modeling (MMM), incrementality testing, attribution, and MER into a single measurement framework, this guide provides a solid overview:
Whether you use a spreadsheet or a dedicated measurement platform, the key is measuring what actually drives profitable growth rather than relying only on what individual ad platforms report.
There isn’t one metric that defines a successful marketing campaign. It varies based on the campaign goal and its effect on the business.
For paid campaigns, focus on metrics like ROAS, CAC/CPA, conversion rate, and average order value. These help you see daily performance and identify which campaigns or creatives work and which need fixing.
However, don’t just trust the numbers from advertising platforms. Their attribution can give an incomplete view of actual business performance.
For a wider perspective, track blended metrics like total revenue against total marketing spend. Also, factor in the campaign’s true profitability. Consider refunds, returns, cancellations, discounts, product costs, shipping, payment fees, and other expenses.
A practical approach is to evaluate performance at two levels:
For instance, a campaign might show strong ROAS but may not be profitable if it has many returns or relies on discounts. Conversely, a campaign with a lower initial ROAS could still be worthwhile if it brings in customers who make repeat purchases.
First, define your campaign goal, then select the right metrics. If your eCommerce store aims for profitable growth, focus on customer acquisition cost and actual profit alongside platform-reported ROAS.
What’s the main goal of your campaign? Is it immediate sales, acquiring new customers, or boosting repeat purchases? This will help you decide which metrics to track.
One useful pass/fail threshold that is often missing from campaign dashboards is break-even ROAS based on contribution margin.
Break-even ROAS = 1 ÷ contribution margin rate
If an order keeps 40% after product cost, shipping subsidy, payment fees, and discounts, break-even ROAS is 2.5x before overhead. If a promotion reduces that contribution margin to 25%, the same campaign now needs 4.0x ROAS just to break even. This is why a campaign can look successful in Meta while losing cash.
I would use three layers:
Keep the attribution window consistent for comparison, but calculate the economic threshold outside the ad platform. Recalculate it whenever the offer changes, especially for discounts, bundles, or free shipping.
Full disclosure: I run OfferVerdict and built a free promotion-profit calculator around this approach. The calculator and limited early-customer audit are available through my OfferVerdict profile/Ask & Offer post. No call or store access is required.
Hi @Arcs
I usually look at a combination of metrics rather than relying on just one. ROAS tells me how efficiently ad spend is generating revenue, MER (Marketing Efficiency Ratio) shows overall marketing performance, CAC (Customer Acquisition Cost) helps me understand how much I’m paying for each new customer; CVR (Conversion Rate) highlights how well traffic converts, AOV (Average Order Value) indicates the long-term value of acquired customers.
For attribution, I don’t rely on a single model. I compare platform attribution with Shopify Analytics to get a more balanced view, since many customers interact with multiple channels before converting. Success thresholds vary by business, but I generally want to see stable or improving ROAS, a sustainable CAC relative to LTV, and consistent growth in revenue rather than focusing on any single metric. A dashboard that combines ad platform data with Shopify Analytics usually provides the clearest picture of overall performance.
I usually look at ROAS first, but I don’t really trust it in isolation. A campaign can look great on paper because the first purchase is profitable while the customers themselves aren’t particularly valuable, so I’ll usually look at CAC and repeat purchase rate once there’s enough volume to make that useful.
I also break it down by landing page. We’ve had campaigns where the targeting and creative were fine, but the product page was doing a terrible job of converting the traffic. That’s easy to miss if you’re only looking at the ad platform dashboard because the campaign gets blamed for something that’s happening after the click.
For attribution, I tend to keep the platform’s numbers for comparing campaigns within that platform and use Shopify/GA4 for the broader picture. I don’t think there’s much point arguing over whether a sale belongs to Meta or Google when the bigger question is whether the traffic is producing profitable customers.
One thing I’ve started doing on the SEO side too is looking at revenue by page rather than just organic sessions. SiteGuru has been useful for that because it brings the Analytics revenue into the site audit, so you can see that a page gets a lot of traffic but barely contributes anything while another page with less traffic is actually making money. That changes which pages I’d prioritize pretty quickly.
I would start with a spreadsheet before adding another dashboard.
The minimum fields I would track by SKU are:
SKU
selling price
average discount
tax/VAT included in price, if relevant
product cost
shipping cost
packaging cost
payment processing fee
refund rate or refund reserve
ad platform CPA
Then calculate:
contribution before ads
max CPA
break-even ROAS
profit after ads
decision: scale, watch, pause, or fix
The important part is SKU-level tracking. Store-level MER/ROAS is useful, but it can hide which products are actually funding the account and which ones are eating the budget.