How to Tell Apart Common Business Metrics

Most marketing reports only focus on easy-to-get metrics. Not those that measure growth + profitability. Metrics like... - ROAS from ad platforms - Traffic & CVR from Google Analytics - Revenue (Net Sales) from Shopify They're simple to pull. Your team checks them daily. Copy/paste into the report. But these metrics don't give you the real picture. Instead, we need metrics that tell us: - Are we growing? - Are we efficient (profitable)? ===== Here's some (better) metrics that actually reflect your business health: 1. Marketing Efficiency Ratio (MER) Calculate: Total Revenue / Total Marketing Spend (not just ad spend) Shows true marketing productivity regardless of attribution. --- 2. New Customer Rate Calculate: New customers ÷ Total active customers (eg. L12 mo) If this drops, you're not growing your customer base. You need to continually replenish your churned or lapsed customers. Note, Also good to look at the % of revenue coming from new customers. --- 3. Customer Acquisition Payback Period Calculate: Months to recover fully-loaded CAC from contribution margin *Note this is challenging to calc. at the customer-level as the majority of your customers won't "pay back". You'll want to look at aggregate curves here. Depends on the business model, but typically 3-6 months = healthy. 12+ months = you might be over-estimating your LTV, and never get payback. --- 4. Contribution Margin per Customer (First 90 Days) This is actually just "90-day LTV:CAC" but when most people say "LTV" they really mean "Lifetime Revenue" or "Lifetime Net Sales". Calculate: (Revenue - COGS - fulfillment - returns - marketing) / New customers in that period Shows actual profitability per acquisition. --- 5. Monthly Cohort Retention (90-day) Calculate: % of Month X customers who purchase again within 60 days Predicts long-term health better than any other metric. --- 6. (Bonus) Customer Concentration Risk Calculate: % of revenue from the top 10% of customers High concentration = fragile business model. The customers you think are the most loyal actually have some of the highest chance of brand-switching. ===== It's all about efficient growth. These metrics answer: "Are we acquiring good customers profitably and retaining them?" Everything else is noise. What else would you add? #marketinganalytics #ecommerce #profitability

Every head of marketing I talk to is fixated on the same 3 vanity metrics. • Platform ROAS • Repeat sales percentage  • Conversion rate But these aren’t the true indicators of business growth. They account for local optimization – even when they're in the green, they might not impact your business health. Here are 3 metrics you should focus on instead: 1. Instead of platform ROAS, focus on Marketing Efficiency Ratio (MER) Platform ROAS (e.g. Meta reported revenue ÷ Meta ad spend) only tells part of the story. The problem: Add up platform-reported revenue across channels, and you'll often get 2-3x your actual revenue. This is the fundamental error in attribution. All platforms claim conversions for themselves. Marketing efficiency ratio (total revenue ÷ total ad spend) shows true performance because it's objective – real money in versus real money out. 2. Instead of repeat sales %, focus on repeat purchase rate by cohort Repeat sales tells you what portion of this month's sales came from existing customers. It's backward-looking and easily distorted. I recently talked to a brand with 40% of sales coming from repeat customers. Sounds healthy, right? But their cohort retention showed only 10% of customers returned after a year. Not healthy. This brand had been in business for 10 years but wasn't adding many new customers. Their seemingly healthy 40% repeat purchase rate masked an acquisition problem. The problem is that repeat purchase rate combines all historical customers into one bucket, hiding true retention patterns. Cohort retention follows specific customer groups over time, showing exactly how loyal different segments are. 3. Instead of conversion rate, focus on pure revenue growth I constantly get asked: "Is my 0.8% conversion rate good? Is 3% good?" That percentage alone doesn’t tell you much. When traffic grows, conversion rates naturally decline. Some of Polar Analytics 🐻❄️’s most successful clients – doing hundreds of millions in revenue – have conversion rates below 1%. From the outside, that might seem terrible. But they're crushing it because their revenue is growing 2x year-over-year, and that's what matters. Platform ROAS, repeat percentage, and conversation have their place for tactical optimization but won't show your true business health. Marketing efficiency, cohort retention, and revenue growth will. If you need help setting up these metrics, DM me.

You can't run your business if everyone isn't aligned on the metrics you use to run that business. A very common example we see is when “win rate” and “conversion rate” are used interchangeably, but the calculations of these can be different and can give you conflicting messages about what is happening in your business. 🤓 Win Rate = Wins / (Wins + Losses) Win rate is generally calculated as the total number of wins divided by the sum total number of wins plus losses for a specific period of time. If you don’t have processes where the Sales team has clear and consistent criteria on when to open opportunities as well as to regularly monitor and close old opportunities, this metric may lead you down the wrong path. For example, if you have a bunch of stale opportunities that are still open but are never going to be won or closed, the win rate you’re looking at will be inflated. 🤓 Conversion Rate = Wins from a Given Pipeline Amount / Total Pipeline Amount Opportunity conversion rate can be calculated as the wins for a given time period over the total pipeline cohort for that time period. This metric can be broken down into components to give you indicators on whether you have “tumbleweed” pipeline that moves from quarter to quarter vs. how much in-period business (a.k.a. “Create and Close”) you rely upon to hit your numbers. It can serve as an excellent leading indicator of whether you have enough pipeline to hit your revenue goals. Or it might demonstrate the weakness of a particular cohort of pipeline that was created from a specific campaign (e.g. You created a bunch of pipeline from that trade show you went to, but none of it ended up closing. Maybe you skip that show next year.) Know your numbers. Otherwise the story you think your data is telling you might not be the right story at all.