Lifetime Value: Using LTV to plan marketing campaigns

App developers swim in a sea of metrics.

Martin Macmillan
6 min readJul 18, 2017

In our data driven world there is no shortage of analytics and attribution packages that allow us to drill down into the detail to an incredibly granular level of detail. But how should developers make sense of these, especially when it comes to the decision to invest in paid marketing?

At Pollen VC, where we provide growth financing for user acquisition to high potential app developers, our whole worldview is based around the metrics of app marketing. We work every day with our developer partners to help them make sense of the sea of metrics available to them and help them take an informed view on Lifetime Value (LTV) calculations, primarily to help make sense whether UA makes sense for their app or not.

For a full overview of the importance of LTV, I recommend you dive into a previous post on this metric. Put plainly, understanding LTV underpins the dynamics of every successful marketing campaign.

App marketers must ask themselves:

“What are these metrics telling me about my app, and what are the actions I should take to help my business grow?”

Looking at LTV, a smart marketer should be able to understand the following questions:

  • Do I understand what my headline LTV is?
  • Do I understand how long it takes for me to achieve it?
  • Do I understand the shape of my LTV curve and what that means?
  • Do I understand the parameters that influence my LTV over time?

Let’s run through each of those points.

Headline LTV. How are you calculating your LTV? Do you have an internal formula you use? Do you use an online calculator? Whatever assumptions you use to calculate your LTV, make sure it’s strong enough you can believe in it in building your plans, by backtesting the assumptions with early cohort data.

How long it takes me to achieve it. How many days does it take to break even on ad spend and where should I draw the line in terms of where the marginal output tails off to the point that I should no longer factor it into my calculations? LTV is not a metric that can be viewed in isolation. It’s very important to know how long it takes to achieve the delivery of expected value as this sets the tone for the overall investment formula.

Shape of my LTV curve and what that means. Can you plot the shape of the LTV curve? If you are able to extract LTV quickly in the lifecycle from users, that can set the tone about how quickly you can reinvest. Some genres will have long slow payoff cycles — e.g. strategy games — where other categories — e.g. arcade games — may have a quickler payoff cycle.

LTV changes over time. This is the understanding that an LTV calculation is just a snapshot of a proposed trajectory of an app. As the metrics change — say a content update improves monetization — this can change your LTV over time. Smart marketing managers calculate their LTV constantly to see how it evolves over time based on the product and the audience acquired.

Putting LTV to work

When you have a full understanding of your LTV metric, you can then shift focus to improving your acquisition and finding the right audience that are most likely to deliver that expected LTV. For example, let’s target Cost Per Install (CPI), or increasingly Cost Per Action (CPA) — this is the cost you pay ad networks to acquire users who then complete a particular action in the app — e.g. makes a purchase, or completing a tutorial.

Increasingly, ad networks are trying to help developers optimise what’s behind the install — targeting users who will actually spend — thus making the Return On Investment (ROI) more transparent.

By working with ad networks in your targeting, you can improve your CPI/CPA, and strengthen your LTV.

There are other ways to change the health of your LTV curve.

Many developers find techniques to increase their Average Revenue Per Daily Active User (ARPDAU) or increase their retention figures. By working with the content and product management teams, you can come up with product KPIs to shoot for in a subsequent release.

For example, making tweaks to a game to improve day 7 retention by sending targeted push notifications containing a promotion to re-engage the user after a period of initial drop off, or by running a time-sensitive sale of in-app currency.

Let’s run through a sample calculation. Through this process one can see how unit economics and an understanding of CPI impact planning for a UA campaign for an app.

Example #1:

I am an app developer with a game that is proven to be fun and engaging for users. We know that our CPI is $1.00. Our retention is pretty good at 40% at day one, dropping to just 5% by day 30, but ARPDAU is pretty healthy at $0.25. We budget $10,000 in User Acquisition. Punching these figures into Pollen’s LTV calculator, we find that the LTV of our game is $1.72 reached after 123 days (95% cutoff point). I can see that my breakeven day on my CPI cost is reached after just 26 days, and a total ROI of 72%.

Remember, this is the LTV calculated as the game stands today, based on a static set of assumptions. As the developer, I may look at this and remind myself that with further updates I should recalculate my LTV regularly, especially with content updates and patches to the game itself, and as I acquire data from more cohorts.

Example #2:

I have been able to make some changes that improve early retention. I am now able to get users paying for content earlier in my game than previously. By making an improvement of 5% to my day 7 retention, and 2% to my day 14 retention (whilst keeping later retention and ARPDAU static), I have shortened my breakeven period to just 19 days and increased my overall LTV to $1.86, giving me a 14% increase in ROI at 86%

It is crucial that app marketers constantly test and retest their LTVs as they have more cohort data available. Even relatively small changes to retention of ARPDAU can have marked differenced to the LTV. If longer term retention can be improved it can skew the results quite dramatically so we would always urge developers to be more cautious in modelling LTV numbers beyond 90 days, or at least make sure there is cohort data to support this longer term app usage.


Successful app marketers must invest time in understanding the metrics available to them and how to make informed marketing decisions with that information. Key to success is constantly revisiting and retesting LTV calculations, as well as constantly trying to improve the quality of the audience whilst reducing cost of acquisition.

As ever this is a combination of art and science, making sure there are always fresh and engaging ad creatives, but also a laser focus on the numbers. These help marketers double down on what’s working and kill early campaigns that are not performing.

This article originally appeared on Priori Data



Martin Macmillan

CEO & Founder at Pollen VC - London, we provide devs early access to revenues earned from the app stores so they can rapidly reinvest