The user opt-out option for Apple’s Identifier for Advertisers (IDFA) in iOS 14.5 has caused a lot of commotion in the eCommerce world. Previously, apps could collect and share data with third parties based on a unique identifier for your iPhone called an IDFA. Now, users can decline to have their activity tracked in this way. And companies selling direct-to-consumer (DTC) are experiencing higher customer acquisition costs, making the economics of the business model more challenging. You can read more about this as it relates to Facebook here.
The central question around IDFA is whether it is causing a measurement problem or a performance problem. Are the platforms unable to attribute purchases to users who were served ads, or is the targeting worse and the ads less effective? So far, it’s unclear based on what the companies are saying:
“[Our] first-party measurement tools and studies continue to show that our ads are effective and they are approximately as effective as they were prior to these changes. So, it’s really that the loss of signal required significant changes to our overall technology, and we believe it’s actually mostly a measurement challenge, not a question of the efficacy of our advertising, and as we talked about before, it’s going to take time for advertisers to sort through what they are seeing in their new measurement solutions.” – Jeremi Gorman SNAP Q3 2021 earnings call (10/21/2021)
“There are two big challenges coming from these iOS changes. One is targeting and one is measurement…On measurement, we think we can address more than half of that under reporting by the end of the year and make more progress in the years ahead. We estimate we’re underreporting iOS web conversions…Targeting is a longer-term challenge…So we have to rebuild our targeting and optimization systems to work with less data.” – Sheryl Sandberg FB Q3 2021 earnings call (10/25/2021)
Bottom line, the boots-on-the-ground feedback we are hearing from companies is that a dollar spent on online advertising is not going as far. This manifests in the P&L at the end of the month with marketing spend increasing as a percentage of revenue. Regardless of platform-reported conversion rates and purchases, companies should see customer acquisition cost or CAC (measured as total paid ad spend divided by new customers) remain roughly flat if performance is not the problem. But that’s not what we are hearing or seeing.
The issue is complicated by the fact that cost per thousand impressions (CPM) are up versus last year as basically every company realized they needed to sell things online.
More online ad spend, more auction intensity, higher CPM prices.
So the higher customer acquisition costs could also be driven by higher CPMs even if conversion rates (reported or otherwise) are unchanged.
With all this in mind, we thought it could be useful to explain our simple approach to thinking about the economics of selling on eCommerce (DTC only) and what “good” looks like.
CircleUp Growth Partners uses a simple framework to evaluate eCommerce metrics: how much profit does a company get from the average customer over a year (“LTV”) and how much does it cost to acquire a customer on average (“CAC”). With all the current noise in reported metrics from ad platforms, we think these KPIs, which can be derived from the P&L and internal metrics, are relevant now more than ever.
There are four pieces of information we need to arrive at this version of LTV/CAC:
| Piece of information | Metric (name) | Example value | Example calculation | Description |
| 1. How much does the average customer spend per order? | Average order value (AOV) | $50 | ($5,000 net sales) / (100 orders) = $50 per order | Net sales over a period divided by total number of orders over the same period (typically trailing 12 months) |
| 2. How many times in a year does the average customer order? | Average orders at 1-year | 1.5 | (150 cohort orders in 12 months ) / (100 customers in cohort) = 1.5 orders per year | Total number of orders placed over a 12-month period by a cohort of customers acquired 12 months ago |
| (1) x (2) = (A) | Expected revenue at 1-year | $75 | ($50 per order) x (1.5 orders per year) = $75 revenue at 1-year | Calculated from above |
| 3. What is the profit margin on each incremental dollar of eCommerce revenue? | Contribution margin | 40% | 1 – [($1,500 COGS + $1,300 shipping /fulfillment + $200 selling fees) / ($5,000 net sales)] = 40% | Margin on eCommerce sales after variable costs, including shipping, fulfillment, fees, etc. |
| (A) x (3) = (B) | Expected profit at 1-year (LTV) | $30 | ($75 revenue at 1-year) x (40% contribution margin) = $30 | Calculated from above |
| 4. How much does it cost to acquire a customer? | Customer acquisition cost (CAC) | $20 | ($2,000 paid ad spend) / (100 new customers) | Paid ad spend over a period divided by the number of new customers acquired during that period |
| (B) / (4) = (C) | LTV/CAC | 1.5x | ($30 profit at 1-year) / ($20 customer acquisition cost) = 1.5x | Calculated from above |
While the measure is retrospective rather than real-time, we consider it to be the ultimate source of truth for companies selling their products on the internet. At the end of the day, every company using eCommerce as a primary sales channel will be assessed on their ability to generate more than $1 in profit on every $1 spent on advertising. And the P&L should tell the story.
This framework gets to that point — an LTV/CAC greater than 1.0x means you are making money within one year on each customer you acquire. There are two other reasons we like it. First, it allows for comparisons across different types of purchasing models. Transactional, subscription, a mix of both — no problem, looking at the average number of orders made by a cohort of customers over a year naturally takes into account mix of customer types and makes the resulting numbers comparable. Second, you can understand why companies have better or worse economic profiles — larger orders, more orders, higher margins, lower customer acquisition costs.
Below are benchmarks representing “above average” eCommerce performance based on what we have seen recently by fast-moving consumer goods category. The component pieces that go into the LTV/CAC calculation can vary greatly, even within categories, so they should only be viewed directionally. The LTV/CAC figures represent the median of high-growth, emerging brands over the past 12-24 months.
One thing that is evident from the table above is that the economics of selling certain types of products online are much more attractive than others. Namely, VMS and personal care/beauty products with (i) higher order values, (ii) habitual use patterns, i.e. high number of orders, and/or (iii) high margins/low shipping costs lend themselves well to the DTC model. Food and beverage products are more challenged, partly due to the relatively lower value to volume/weight of a bag of potato chips or case of sparkling water vs. a tube of lipstick.
There are many legitimate reasons for brands to provide consumers with the option to buy things online beyond achieving a profitable sale. Selling DTC allows brands to test products before making expensive forays into retail and to reward loyal customers with the latest products. And online advertising can drive higher retail sales. If the eCommerce business is not directly profitable, understanding the broader online strategy and attempting to measure the omnichannel return (not an easy task!) of online advertising are things that are top of mind for us.
Concerns about the long-term economic viability of eCommerce-only businesses are not new given the diminishing marginal unit economics. As brands scale out of an initial core audience, LTV tends to fall as they attract less loyal customers while CAC rises from having to work harder to convince a new customer to try the product. While this is often true, we think it somewhat misses the point because eCommerce is a proven way for brands to connect with and learn from an initial user base, profitably reach scale quickly, and potentially complement traditional brick-and-mortar down the line.
We hope the framework above can serve as a way for brands to understand when to invest more or pull back the reins on customer acquisition, especially as the effectiveness of paid advertising remains an open question. If last month’s LTV/CAC was less than 1.5x, it may be a signal to reevaluate investments to drive top line growth. Or consider a different paid marketing mix by channel and alternative forms of brand building (partnerships, content, community) to drive LTV higher and/or CAC lower before making further investments.