Much like death and taxes, one thing you can be assured of is that UA metrics will change and evolve over time, and not remain static and predictable. Specifically, two things will start to happen as a business starts to scale through paid acquisition - acquisition costs will increase and lifetime values will decrease.
- Acquisition costs will rise - as you spend increasing amounts on platforms like Facebook and Google to acquire users for your app, costs per acquisition will increase. Whilst slightly counter-intuitive at first glance, in reality, you are bidding in an auction where there is an ever-decreasing pool of target users, hence you have to bid higher amounts to be successful in the auctions in acquiring these installs or actions
- Lifetime Values will fall - the logic here is that incremental customer retention and spending will fall as your marketing reaches users beyond your ideal target audience. For example, if you have a game based around a particular Hollywood IP, then diehard fans will be your highest payers, gradually diluting as the level of interest in the artist/celebrity falls away to be more passive.
Analytical user acquisition (UA) managers and finance teams look at the cohort data back from their ad spend and users, and try to figure out a max acquisition cost that they are prepared to buy up to in order to achieve a minimum target ROI, beyond which the numbers may get too close to call.
For example, a UA team may decide to seek a minimum ROI of 15% to allow them some wiggle room in case of lower than expected cohort performance.
The concept of the maximum capacity of the acquisition machine is figuring out the equilibrium point for your app to continue to acquire users - going beyond that point becomes risky or uneconomical to spend.
Key factors that impact the ability to call the numbers include the breakeven day of ad spend and the overall LTV profile. For example, certain game genres may have shorter LTV profiles and thus UA managers have a greater level of confidence in getting the numbers right.
Other genres such as strategy or social casino games have much longer LTV profiles, and therefore much more data is required before making these investment decisions and the risk of getting it wrong is higher.
Think of it like throwing a ball out of the window. The more often you throw it, the easier it is to predict where it’s going it’s going to land.
Addressable market impacts capacity
The size of the addressable market for any game or app is a key determinant in how quickly maximum capacity will be reached. It’s important to try to plot out graphically what the CAC and LTV parameters look like in each case, much like a demand and supply curve in economic theory.
The graph below shows how CAC and LTV are impacted for a niche app, where acquisition costs rise quickly and where LTVs crest quickly as additional users are acquired due to the lower propensity to spend as the core audience is exhausted quicker.
The second graph shows a much larger addressable market where more users can be acquired without such a negative impact on CAC, and also where LTVs fall at a slower rate as the core highest paying audience erodes.
The shaded area indicates the danger zone. Up to the danger zone, UA managers should acquire as many users as possible as the expected CAC/LTV is in positive territory.
Responsible media buyers will leave a margin of error in the buying calculation to reduce the risk of slipping into negative ROI. For example, they won’t continue to spend unless they see a minimum expected return of 15%.
For a mainstream hyper-casual game, it may be able to reach a huge scale quickly because of low acquisition costs due to the broader nature of the audience and short LTV recovery periods.
However for a more niche app or game, full capacity is going to be lower by definition - the overall addressable market of people potentially interested in the app is smaller, therefore there is a limit to how many of them can be reached on any particular day in time.
Timing impacts capacity
Daily limits also play a part here.
On any given day there will be only a certain subset of your total addressable market as an audience who could install the app. Again this daily audience will depend on the overall market, with niche audiences being exhausted more quickly, but offering a huge scale for daily user acquisition by the apps with the broadest appeal.
Lastly, seasonality will also impact how these numbers change over time.
If demand is high (ie around the holiday season), daily performance monitoring is critical as CAC will increase more rapidly as there is greater demand competing for engaged users which in turn will impact the equation.
Conclusion
To recap our 3-part blog series on the user acquisition machine:
- First establish IF you have an efficient acquisition machine
- If you have built a machine, figure out how you are going to feed it. Where can you find the capital in order to spend as much as possible until it is running at full capacity?
- Once you establish what full capacity is - don’t spend beyond this daily/weekly/monthly limit to ensure you don’t spend inefficiently
- Monitor your stats as frequently as possible, at least weekly as full capacity will change over time
This blog post is part of our user acquisition machine series.
Part 1 – How to Analyze User Acquisition Metrics Like a Hedge Fund Manager
Part 2 - The Capital Stack: How to Optimize User Acquisition Funding
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