Diseconomies of scale in digital advertising

A common pattern of progression for a user acquisition team is to start with one person who handles everything, grow the team as revenues increase and / or the company increases the number of products in its portfolio, and build out an analytics architecture to streamline and centralize reporting and analysis. The general idea behind this course of evolution is that the team’s analytics infrastructure will make people more productive and decrease administrative overhead, and as more and more money is spent on user acquisition, more can be invested into developing tools, making every additional dollar spent more productive than the previous.

This sounds reasonable in theory, but it often doesn’t work out this way. User acquisition tends to suffer from diseconomies of scale, meaning, after some point, each dollar spent generates less utility than the one before it; in other words, the average efficiency of the user acquisition team decreases as the team’s budget and headcount scale. Note that this is different than margin on spend; it’s the carrying cost of the team and the budget spent on user acquisition combined, as a percentage of revenue.

This diseconomy of scale — which is the opposite of an economy of scale, which is a cost advantage due to size — tends to happen for a few reasons. The first is the most prevalent: at some budget size, user acquisition teams have to diversify traffic outside of the top-tier channels and into lower-scale points of supply. The smaller-scale sources of traffic may introduce operational challenges that decrease team efficiency, eg. they’re much more susceptible to fraud, they tend to not feature robust APIs for data collection, and they often don’t allow for self-service campaign management (meaning everything is run through an AM). All of these issues make working with them tedious and sluggish: campaigns are slow to be optimized and have to be heavily scrutinized for fraudulent traffic.

The second reason that this diseconomy of scale emerges is that the diverse range of inputs that an analytics infrastructure needs to accommodate as the team grows creates highly fragile dependencies that are prone to breaking down. With a one-person team spending a budget in the hundreds of thousands of dollars per month, reporting is a one-day process: the user acquisition manager logs into their networks once per week, downloads the relevant performance data, compiles it into a workbook, and evaluates spend. But as the budget and team scale and the company builds out infrastructure to automate this, given the state of the marketing services and tools ecosystem, it runs the risk of creating a frail system that breaks down and / or produces incorrect data regularly.

This generates more overhead for the team, not less: now the team, which has become reliant on their proprietary system for managing campaigns, has to not only frequently wait for their analytics system to be fixed, but they have also been tasked with staying in constant communication with the analytics team in order to stay abreast of downtime, errors, and updates.

And a third cause of the diseconomy of scale relates to hiring and team churn (which I also discussed in Why mobile marketing teams fail): the user acquisition labor market is extremely fluid and active, meaning junior team members are under constant threat of being poached. This creates a level of recruiting overhead which can be dramatic: constant interviews and CV evaluations can consume a substantial amount of the team’s time. As a budget scales to require a bigger team, more and more of the team’s time must be spent on recruiting — not just for hiring new headcount but also for replacing churned headcount.