The 5 marketing questions every e-commerce brand and lead business should answer every Monday morning

Most e-commerce marketers end the week by glancing at a dashboard, feeling vaguely satisfied or vaguely worried, and moving on. That's not a review process — that's just looking at numbers. A proper weekly review takes 15 minutes, answers five specific questions, and produces actual decisions. Here's the framework.

Why Monday morning, not Friday afternoon

The instinct is to review on Friday — the week is done, the data is in. But that's backwards. A Friday review tells you what happened. A Monday morning review lets you change what will happen.

Ad budgets run all week. If you discover on Friday that a campaign has been bleeding money since Tuesday, you've already lost four days of spend. If you discover it Monday morning, you can act before a single dollar of the new week's budget is wasted. Monday morning is when decisions have leverage.

The review should cover last week's data — pulled on Saturday or Sunday when all attribution windows have settled — but actioned on Monday before campaigns are fully underway. Ideally you're looking at this before 10am.

The 5 questions

Q1: What was my blended ROAS last week vs my target?

This is your single top-line health check. Blended ROAS = total Shopify revenue attributed to ads divided by total ad spend across all channels. Not platform-reported ROAS — Shopify-truth ROAS, pulled from actual order data.

Where to find it: DataMaster's overview dashboard shows blended ROAS week-over-week with your target overlaid. Alternatively, pull last week's Shopify revenue from orders (filtering for orders attributed to paid channels via UTMs) and divide by total spend from your ad platforms.

What to do when it's off: If you're more than 15% below your ROAS target, you need a budget rebalance this week, not next. Identify which channel is dragging the blended number down (that's Q2) and reduce its budget proportionally. Don't wait for the weekly cadence to fix it — a 15% ROAS miss compounded over four weeks is a serious problem.

Q2: Which campaign had the worst Shopify-truth ROAS?

Every week, at least one campaign is underperforming relative to the rest. The question is whether it's underperforming enough to act on. Platform-reported ROAS will mislead you here — Meta in particular over-reports conversions because of its attribution model. You need Shopify-truth ROAS: the revenue Shopify actually recorded on orders that came through each campaign's UTM parameters.

Where to find it: DataMaster's channel breakdown table shows ROAS by campaign using Shopify order data. In GA4, you can approximate this by going to Reports > Acquisition > Traffic acquisition and filtering by campaign, though GA4 uses last-click attribution which will differ from Shopify.

What to do when it's off: If the bottom campaign is below 0.8x your target ROAS and has spent more than $500 last week, it's either a pause or a budget cut. Don't let sentiment ("we spent months building that campaign") override the numbers. If it's a new campaign still in the learning phase (under 50 purchases), give it one more week before cutting.

Q3: Is my new customer acquisition rate up or down vs last week?

ROAS tells you about revenue efficiency. This question tells you about business health. A brand that's only selling to existing customers isn't growing — it's milking its existing base. New customer acquisition rate is the most forward-looking metric in this list.

Where to find it: Shopify's customer reports distinguish new vs returning customers. GA4 also segments new vs returning users under Audience. For a clean number, look at Shopify orders last week and count what percentage were from first-time customers.

What to do when it's off: If new customer percentage drops below 30% of total orders for two consecutive weeks, your prospecting budget is too low relative to retargeting. Shift at least 20% of retargeting budget into prospecting audiences. If it drops below 20%, that's a red flag — your existing customer base is finite and you're not replacing churn.

Q4: Did any channel's CPA exceed my max CAC threshold?

This is your guardrail check. You should have a maximum acceptable cost per acquisition for each channel — the number above which a new customer is unprofitable given your average order value, margins, and expected LTV. When CPA exceeds that threshold, you're buying customers you can't afford.

Where to find it: Ad platforms report CPA, but use their own conversion counting (which may include view-through conversions and other inflated signals). For a reliable number, divide last week's spend on each channel by the number of new first-time orders attributed to that channel in Shopify.

What to do when it's off: If CPA exceeds your max CAC on any channel, that's an immediate budget pause or creative test — not a "let's keep an eye on it." Either the creative has fatigued, the audience is exhausted, or the offer isn't converting. Pause the top spenders in that channel, launch a creative test with fresh angles, and don't restore full budget until CPA is back under threshold for at least three days running.

Q5: What does the data suggest I do differently this week?

The first four questions are diagnostic — they tell you what's wrong. This question is strategic — it asks what opportunity you're missing. It might come from trend analysis (a product that's getting traction without much ad support), seasonality (a category that typically picks up this time of year), or competitive signals (a spike in branded search suggesting competitors are running ads against you).

Where to find it: If you use DataMaster, the AI analyst surfaces one or two prioritised recommendations each week based on your data patterns. Without that, spend five minutes looking for anomalies: anything with an unusual uptick in revenue, any channel that improved significantly, any product that sold better than expected.

What to do: Implement the top recommendation this week, not eventually. If the AI or your own analysis suggests shifting budget to Google Shopping because ROAS improved 40% week-over-week, do it today. Delayed decisions are no decisions.

If you run a leads business, use these variants for Q1 and Q4

The five questions above are framed for e-commerce brands where the transaction happens online. If your business closes sales offline — via phone, in-person, or through a sales rep — Q1 and Q4 have different forms:

Q1 (leads variant): What was my closing rate last week vs my target?

Instead of blended ROAS, a leads business should track closing rate (orders ÷ prospects). DataMaster's leads dashboard shows closing rate automatically — no spreadsheet required. If your closing rate target is 28% and last week you hit 21%, that's a Q1 red flag regardless of what your ad ROAS shows.

Q4 (leads variant): What was my cost per order last week?

For leads businesses, CPA (cost per acquired customer) is replaced by cost per order — total ad spend divided by closed orders including all offline revenue. DataMaster tracks this from the full CRM/Callbox pipeline, including orders that closed by phone days or weeks after the original ad click. Without this, your "cost per lead" metric is divorced from revenue.

The decision matrix

The 15-minute rule

This review should take 15 minutes. Not two hours, not 45 minutes — 15 minutes.

The enemy of a useful weekly review is scope creep. If you go in without a structure, you end up following rabbit holes: clicking into individual ad sets, comparing creative performance, re-reading last month's data. An hour later you've generated a lot of anxiety and made zero decisions.

The five questions are a forcing function. They have specific answers, specific thresholds, and specific actions attached. You either hit the threshold or you don't. You either act or you don't. There's no room for extended deliberation.

The discipline is not opening anything else during those 15 minutes. You're not optimising individual ads — you're making portfolio-level decisions. Save the deep dives for when you have an hour blocked specifically for that.

How DataMaster automates the first four questions

Questions 1 through 4 are fundamentally data collection and comparison tasks. They require pulling numbers from multiple sources — Shopify, GA4, Meta, Google — and comparing them against targets you've set. That's exactly what software should do, not you.

DataMaster connects to all four sources and assembles the Monday morning view automatically. When you open it on Monday morning, Q1 through Q4 are already answered: blended ROAS vs target, worst-performing campaign by Shopify-truth ROAS, new customer rate vs prior week, and any channel where CPA exceeded your CAC threshold. The numbers are there. The thresholds are highlighted. All you're doing is deciding.

Q5 — what to do differently — requires judgment. The AI analyst in DataMaster will surface a recommendation, but you should interrogate it. Is the suggested channel shift consistent with what you know about your business this week? Is there a seasonal factor the model isn't accounting for? That's the five minutes of genuine strategic thinking this review requires. The other 10 minutes should be pure execution.

The brands that grow consistently aren't the ones with the most sophisticated attribution models or the most complex dashboards. They're the ones that make small, correct decisions reliably, week after week. Five questions, 15 minutes, every Monday morning.