The Reformation Partners 30/50 Rule of DTC Efficiency

In years of working with DTC companies selling everything from contact lenses to bed sheets to hair loss meds, eveningwear, and prepared meals, we noticed a pattern around unit economics that correlates with efficient growth. We call it The Reformation Partners 30/50 Rule of DTC Efficiency, or in short, The 30/50 Rule.

The 30 refers to the dollars of gross profit net of CAC on first order. The 50 refers to the cumulative dollars of gross profit net of CAC within the first year. Companies that achieve $30 gross profit net of CAC on first order and follow that up with $50 cumulative gross profit net of CAC within the first year are well positioned for rapid and efficient growth.

As long as companies get close to The 30/50 Rule, keep their overhead lean (see post on utilizing outside agencies) and their working capital cycle tight (see post on working capital cycle), they can run a fast growing, efficient DTC company with relatively little need for outside capital.

It’s important to note that not every great company gets to these thresholds, especially very early on when they don’t have much scale to maximize their margins, but they are good aspirational figures like the Rule of 40 is for mature software companies.

This is an analysis we run on every DTC company we evaluate, and we wanted to share both the methodology and a link to The Reformation Partners 30/50 Rule Google Sheet Calculator. Simply input over the red highlighted cells and the sheet will auto-calculate.

Aside from calculating the 30/50 Rule, the calculator can also be used in reverse to determine the CAC envelope, e.g. how high CAC can go based on payback goals (higher CAC means longer payback). This can help inform where to set a CAC limit on Facebook Ad Manager.

The calculator requires just four inputs: Initial AOV, fully-loaded GM, blended CAC, and a first year monthly cohort repeat curve. Many of our readers will know what to input, but for clarity I’ve defined the terms below.

Initial Average Order Value: Take the total dollar value of all first time orders divided by the total number of first time orders. Make sure to count multiple items in the same order as one order. Don’t do overall AOV as it may skew your CAC math, and any rise or fall in AOV over time will be captured separately in the dollar repeat curve anyway.

Fully-Loaded Gross Margin: Include all variable costs including labor, fulfillment to customers, and credit card processing fees. If in doubt, put it in variable costs. Don’t run this analysis with just your product margin. The idea is to set your variable net profit against your fixed overhead.

Blended CAC: Take all variable marketing spend and divide by all new customers acquired, including organically or off Facebook (don’t take Facebook’s paid CAC metric on face). Some of your spend will inevitably be on reacquiring existing customers but that’s fine for this analysis. Attribution and targeting is hard.

Monthly Cohort Weighted Average Dollar Repeat Curve: This one is a mouthful and takes some work. I usually go into Shopify raw data and manually engineer the data file to give me a weighted average monthly cohort repeat rate. More recently I’ve discovered tools like Lifetimely and Glew.io in the Shopify App Store that automate this entirely. I’d recommend getting one of these since this is an important analysis to run all the time to determine LTV (disclosure: I’m not an investor in these other than Shopify, this is not an ad, I just like their tools).

A few notes on calculating LTV and related metrics like CAC payback and LTV/CAC:

• For our LTV and CAC payback analysis we use a weighted average cohort repeat rate that looks like a curve instead of a rolled up monthly repeat rate like what’s shown in the standard Shopify dashboard. The curve is more accurate than the straight line extrapolation for LTV and CAC payback analyses.
• The cohort view is also more useful because it shows which customers are contributing which orders at any given time. That way you can track particular acquisition or retention campaigns, or even variances in product between cohorts.
• We tend not to look much at LTV/CAC ratio as a metric because it relies on too many out-year assumptions for an early stage company. More on this topic in our post on why CAC payback is a better metric than LTV/CAC ratio.
• Last note on methodology: don’t confuse Lifetime Revenue (LTR) with Lifetime Value (LTV = LTR * GM). LTV needs the gross margin factor to properly figure out when you are paid back on CAC and how much to spend on CAC. If you use LTR instead of LTV you run the risk of overstating the return on marketing spend.