Reading & Interpreting Data

What data actually is (and isn't)

Most operators stare at data dashboards every day and make worse decisions for it. They look at numbers and feel like they're "being data-driven" without ever actually using the data to inform anything.

Data is information. That's it. It's not the answer. It's not the strategy. It's not the decision.

If I tap this wall and one spot is more hollow than the rest, and my goal is to break through the wall, I need to hit the most hollow spot. The data tells me which spot is hollow. The data doesn't tell me whether to break the wall. That's me deciding.

Knowing the right data is having the right information to make the right decisions. The wrong data — or the right data without a question attached to it — is just noise. The wall analogy works in reverse too: if your goal is to not break the wall but to hit it without damage, you need different data (where's the sturdy spot, not the hollow one). The data you need depends on the decision you're making.

This sub-page is about how to read data with intent. Which Shopify reports actually matter. How to think about averages, outliers, and time horizons. The Claude workflow for asking the right questions. And why "just be a nerd, bro" is one of the most underrated career moves in DTC.

All data is averages — and averages lie at small sample sizes

Quick framing before the reports.

100 sessions → 2 sales → "2% conversion rate." Mathematically true. Almost meaningless as a predictor.

Add 900 more sessions → maybe 5% conversion rate emerges. Add another 9,000 → maybe it settles at 3.2%.

1,000 sessions is not statistically significant for most metrics. It's a directional read at best. The longer the time horizon and the larger the sample, the more reliable the average becomes — but even then, single outliers can skew it.

The AOV trap: your AOV averages $45. The next day, one customer buys $900 worth of product (corporate gift, retail re-seller, gift bundle, whatever). Your day-2 AOV is now $73. Every other order was identical. One outlier changed your read.

Two implications:

1. Don't make decisions off single-day data unless you're at scale where one customer doesn't move the average meaningfully
2. Always check for outliers before reading averages — one $900 order, one viral creative, one bot spike can hide what's actually happening

The Shopify reports I actually use

Skip the dashboard. Go to Analytics → Reports. This is where the real data lives. Below are the four reports I open most often and what I use them for.

1. Items Ordered Over Time

The use case: real average items per order, which is the input for accurate COGS math.

The trap most operators fall into: "My product costs $5 to make. If I sell two, my COGS is $10. So average COGS is $7.50."

Wrong. A certain percentage of buyers take 1, a certain percentage take 2, a certain percentage take 3+. The actual COGS-per-order depends on the mix.

The right move:

1. Open Items Ordered Over Time
2. Set the date range to your longest meaningful window (30, 60, 90 days)
3. Note the total items ordered and total number of orders
4. Divide → that's your actual average items per order

In my example store: 1.7 items per order across the period. Not 1, not 2 — 1.7. That's the multiplier you apply to your unit COGS for accurate margin math.

Y = MX + B. Slope-intercept form. The math I swore I'd never use in school. I use it constantly now. Be a nerd. The nerds win.

2. Sessions by Location → Conversion Rate by Location

The use case: find where you're winning and where you're bleeding by geography. Critical when running anything beyond US-only.

Default Shopify view is Sessions by City — not useful. Switch to country-level, then add conversion rate as the metric.

My typical averages on cold-traffic advertorial / direct-response funnels:

The trap: running US + UK in the same campaign and looking at blended CVR. If your US conversion is 3% and your UK conversion is 0.9%, the blended 1.8% looks "fine" — but actually you have a high-converting US engine and a leaking UK funnel. Catch this and either fix UK or split it off into its own campaign.

The discipline this teaches: 80/20 / Pareto principle applied to geography. Where is performance coming from? Where are you bleeding? You can only see it by location, not by campaign average.

Save as a custom report in Shopify. Title it something like "CVR by Location." Open it weekly.

3. Sessions by Landing Page

The use case: identify the actual best-converting landing page when you're testing multiple. The single most overlooked report in DTC.

If you're running advertorials, listicles, prelanders, quizzes — you have multiple landing pages live at the same time. You need clean URL naming conventions (advertorial-name-v1, listicle-name-v2, etc.) so this report is readable.

What you're looking for:

• Which landing pages are getting the most traffic
• What their conversion rate is
• The outliers — landing pages with low session count but high conversion rate

The outlier example I've hit personally: every advertorial running at 2-3% CVR, except one at 7% with only 100 sessions. Tiny sample size, but 7% vs 3% is a wide enough gap to investigate.

Cross-check with the ad account. The ads pointed at the 7% landing page weren't getting much spend. Conclusion: the back-end (landing page → checkout flow) is excellent. The constraint isn't the landing page — it's the ads getting traffic to it. That's a different problem with a different fix.

Without this report, you never find the 7% landing page. It dies quietly with 100 sessions and never gets the budget it deserves. The wins hide in the small-sample-size outliers. Get into the reports.

4. Subscription vs One-time Sales

The use case: track your subscription opt-in rate over time, especially after offer changes or page updates.

Quick report, daily glance. If subscription opt-in is shifting down, something on the product page or checkout flow changed. Catch it fast before LTV gets hit downstream.

Pareto principle applied to everything

The 80/20 rule isn't a cliché. It's literally what data analysis is for in DTC.

• 80% of revenue comes from 20% of products → which products? lean into them, deprioritize the rest
• 80% of conversion comes from 20% of landing pages → which landing pages? scale the winners, kill the laggers
• 80% of profit comes from 20% of customer segments → which segments? build retention flows for them
• 80% of waste comes from 20% of broken funnel points → find the leak, fix it

Most operators average everything together and miss the asymmetry. The Pareto distribution is everywhere in your data. Look for it.

Time horizons — why thinking in days kills you

When you launch a product, of course look at day 1. Early signals matter for the kill-or-scale decision (cross-reference Module 4 → How to Properly Run Split Tests for the testing rhythm).

But as the product transitions from "test" to "brand," stretch the time horizon you're making decisions on.

The longer your time horizon, the better your decisions get. Day-to-day operators react to noise. Week-to-week operators react to trends. Month-to-month operators design strategy. Year-to-year operators build empires.

The way to make this shift: stop opening Shopify analytics every hour. Open it once in the morning to check the dashboard, then close it. Open the deeper reports on a weekly cadence. Quarterly reviews for strategic decisions. Most "data anxiety" is from looking at data too frequently with no decision attached.

The Claude data workflow

This is how I learned to actually read data when I started.

The setup:

1. Export your data — Shopify report CSV, Meta ads CSV, Klaviyo flows, whatever you're trying to understand
2. Drop it into Claude (cross-reference Module 3 → Native sub-page for project setup if you're running multiple brands)
3. Ask questions

The questions I asked when I was starting — every single one of these came out of one hour of just sitting with Claude:

• What does this metric actually mean?
• What's the difference between impressions and reach?
• What's the difference between unique outbound click and regular cost per click?
• Which one is more important if I'm trying to scale a top-funnel ad?
• Why is this number higher than this one?
• What's the point of looking at this metric at all?
• What behaviors are leading to the conversions I want?
• If I see X, what should I conclude vs what should I investigate?

The principle: you don't know what you don't know about your own data. Claude does. Ask. Sit with it for an hour. By the end of that hour, you'll understand metrics most operators bluff their way through for years.

This is the cheapest, fastest education in DTC. One hour of asking dumb-sounding questions to Claude is worth more than a $2k course on "Facebook ads metrics explained."

Brandon-rule: the operators who get good at data don't have higher IQs. They just asked more questions earlier. Be a nerd. Loki.

The 19-year-old pitch competition story

Quick story to drive the point home.

When I was 19, I won a senior-level pitch competition at my school — against 25 and 26 year olds. I wasn't smarter than them. I was younger, my mustache was rough, I cut my own hair, my suit was too small because I didn't have the money for one that fit. None of that mattered.

The reason I won: I walked into the room with data.

I'd actually run Google Ads for 60 days before the competition. I went in there and said:

• "Here's what I ran."
• "Here's the money I made."
• "Here's the money I lost."
• "This is what this metric means."
• "This is how I know if I do more of this, I'll get this return."
• "This is what's happening. This is what's going to happen next."

That's the point of data. Everyone else was pitching ideas. I was pitching evidence.

The 25-year-olds had slides. I had a spreadsheet. The judges trust the spreadsheet.

When you go to anyone — investors, partners, agencies, students, employees, your own future self — and you can say "this is what is happening and this is what is going to happen next, here's the data that backs it" — you win the conversation. Build the data muscle. It compounds across every interaction in your career.

Outliers and external events

Some days will be weird. Accept it.

A major news event hits the cycle, a platform pushes an algorithm update, a competitor goes viral, a global event reshapes consumer mood — your data shifts because there are real humans on the other side of the screen. Their attention, emotions, and buying behavior aren't constants.

The discipline: when an outlier day happens, don't react. Note it, set it aside, and look at the trend without that day. Don't redesign your strategy off a single weird Tuesday.

The opposite discipline — and the more important one — is: don't cope.

Brandon-rule on the Discord cope spiral:

When Zuck pushes an update or Meta tightens policies or rates spike, half the operator Discords flood with "anyone else having bad results?" cope. There is always someone still scaling. Always. The platform update isn't why your account is down. Your fundamentals were already weak and the update just exposed it.

The operators who scale through algorithm changes are the ones who treat external events as environmental constants to adapt to, not personal attacks to grieve. Read the data, adjust the strategy, move on. Don't cope.

The outliers ARE the secret winners

Final reframe on outliers, going the other direction.

Most of your wins will hide in the outlier data, not the averages.

Examples from my own ops:

• A landing page with 100 sessions converting at 7% (when the rest were at 2-3%) → restructure ad targeting toward it, becomes a 6-figure month
• A creative with 200 impressions and a 5x ROAS (vs 1.5x average) → scale that ad set, becomes a top performer
• A geography with 30 sessions but 8% conversion rate → spin up a dedicated campaign for that geo
• A customer segment that's 5% of orders but 30% of LTV → build a retention flow specifically for them

Open your reports. Look for the things that don't fit the average. Those are the leverage points hiding in plain sight. Most operators only look at the totals and never find these.

The launch-first principle

One last data principle that contradicts how most people approach this.

Don't over-research before launching. Launch and let the market give you the data.

I sat through senior-level entrepreneurship classes. Won pitch competitions. Got invited to business events with founders who'd raised millions. The one thing everyone said: "Do you have data that backs any of this, or are you just talking?"

You can theorize about a product, an angle, a market, a price point forever. You'll learn more in 7 days of running it live than in 7 months of researching it.

Module 5 covers customer research at depth — that's the qualitative side. This sub-page is the quantitative side. Both feed your decision making. Neither replaces the other. And both compound when you're actually running campaigns, not just thinking about running them.

Launch it. See what the market is telling you. Get your data. Then decide.

What's gatekept inside the masterclass

The principles are here. The live walkthrough lives in the masterclass:

• The full 15-minute video of me walking through the Shopify reports on a live store — exact navigation, where each report lives, what to set the filters to, what the readouts look like
• The Claude data Q&A workflow demonstrated live — me uploading exports and asking questions, the kinds of follow-up questions that surface real insights
• The custom Shopify reports I save and revisit — exact configurations for "CVR by Location," "Sessions by Landing Page with naming convention examples," and the subscription tracking view

Want the full 15-minute video walkthrough, the Claude data Q&A demo, and the custom Shopify reports configurations? All inside the RunItUp Masterclass → Join here

Join now to lock in your rate, prices are increasing quickly.