Last version published: 21/06/2017 10:41
Publication number: ELQ-35617-2
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How to analyse your LTV
You Aren’t Ready For Paid Marketing Until You’ve Done Your LTV Analysis
Introduction
What’s an LTV analysis?
It’s a financial model that helps you calculate the lifetime value of a customer based on expectations about:
-> How much and how often a customer will pay you for your product or service
-> What it will cost you to deliver this service
-> How long the customer will continue to pay you
A quality LTV analysis should capture how customers behave over time and how this impacts profitability, so it’s important to use as much historical data as possible. An LTV analysis will also show you each customer’s value with respect to your business and how much you can spend on acquisition without losing money.
Why it’s important!
Keepsafe’s ability to attract millions of users organically is a testament to building a product people want and a brand people trust. We’ve invested in both over the years. But we also recognize that eventually we’ll need alternate strategies beyond organic growth. We must be prepared to play offense as competitors enter the market and vie for customer attention. Our position as a fast-growing, category-leading company depends upon our ability to build awareness and share Keepsafe’s value with new users. That’s why we felt compelled to explore paid marketing. The LTV model sets the guardrails for a paid marketing strategy.
Before we increased our paid marketing budget, we wanted to be sure there was justification to do so. As a startup, an investment in marketing comes at the cost of other growth tactics. We needed to get clear on what a new app install was worth to Keepsafe in terms of real revenue. We knew that an LTV model would assure that we weren’t wasting precious resources and that our marketing strategy remained profitable. With Open Oceans, the partners we hired to bring a data-driven approach to paid marketing, we built an empirical LTV model grounded in historical data and conservative forecasts.
If your startup is thinking about kicking off paid marketing efforts, especially if you are working with a third-party agency, the time is right to get cracking on an LTV analysis. Additional benefits are:
- Understanding how marketing different products at different prices affects the value you realize from your customers
- Identifying key points in time where customer behavior changed
- Assessing how customer behavior should impact cash management
- Step n°1 |
Step 1: Design first!
If you are a dynamic, early-stage startup like Keepsafe, you’ll need a model that’s customized to your business, not just a traditional LTV formula. (Basic LTV models use point-in-time equations that don’t account for changes in customer behavior, costs, nor revenue.) Since Keepsafe has a global footprint and makes consistent product improvements, we anticipated different lifetime values for customers across varying cohorts, locales, and operating platforms. Keepsafe’s app also uses a freemium model: people can download it for free and subscribe annually for premium privacy features and more storage. So Keepsafe often realizes the value from an install months after a user first downloads the app.
We needed a comprehensive model that would allow us to:
- Generate a perpetual LTV for every Keepsafe install to set a CPI (cost-per-install) target for marketing
- Normalize user cohorts based on monthly installs for comparison
- Benchmark subscription rates and build forecasts for expected future value
- Set assumptions to understand how changes in cohort behavior impact future economics
- Step n°2 |
Step 2: Organize your data
For the best possible analysis, we pulled years of data points extending back to Keepsafe’s launch. Key data included monthly installs, subscriptions, renewals, and revenue. Your initial install figure should always be the basis for your LTV analysis. Churned users shouldn’t be removed, since you paid for them!
We broke cohorts up by month of install and operating system. For example, everyone who installed Keepsafe in December of 2014 on iOS fell into a single cohort. Note: Keepsafe puts users’ privacy first. We anonymize all user data and only use it in aggregate.
We organized the data into a grid for comparison by cohort age, as you can see on the right.
This is vital, because we can see how key improvements to our business like product changes and pricing impact LTV over time. For example, we recognized that a change in subscription packages increased LTV in all cohorts following the change.
Comparing cohorts by age gives visibility to insights and business performance trends that are otherwise obscured.
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