Lifetime value is a metric that helps you focus on long-term value
When assessing the health of a subscription business, there are a dazzling array of KPIs to consider. Many short-term metrics (e.g. conversions, monthly revenue, etc) are helpful, but they don’t give a true picture of the long-term profitability of a business. This is where customer lifetime value (LTV) comes in.
Simply put, LTV estimates how much money a customer will spend over the duration of their subscription. By putting LTV at the forefront of your business, publishers are able to focus on long-term value rather than short term metrics. It is helpful to track LTV over a longer period of time, typically 4-5 years, to keep tabs on the financial sustainability of your business.
At the Financial Times, we calculate LTV for B2C and B2B customers:
B2C LTV - B2C subscribers typically only have a single contract with the FT, so we calculate LTV on an individual basis.
B2B LTV - Unlike with B2C subscribers, we do not calculate B2B LTV on a per-user basis because our contract is not with the user but with the business. As a result, B2B LTV is measured on a per-company basis.
Lifetime value is critical if you want to measure the quality and loyalty of your customers
There are few metrics that are as important for measuring financial sustainability as LTV. Measuring subscriber volume and price alone does not always paint the full picture, as it does not encapsulate the quality and expected loyalty of different customers. For example, it is possible that one high-value subscriber is worth more than three low-value subscribers. Segmenting customers by LTV also can provide you with a deeper understanding of who your “quick churn” customers are.
Lifetime value is calculated by combining Earned Value (historical spend) and Residual LTV (estimated future spend)
LTV has three main components:
- Earned value - This denotes how much money a user has already spent with your company since they first subscribed.
- Residual LTV - This is an estimate of how much money a current user will spend between now and the time they cancel.
- Total LTV - This is the combination of earned value and residual LTV. It is typically what is meant when referencing customer LTV.
Residual LTV is calculated with a predictive model
In order to calculate customer LTV, you must factor in the price a customer will pay and the likelihood of their renewal in the future.
Price is a known variable in the calculation as the model assumes the current price point will continue indefinitely. Calculating price can become slightly more complex if you offer trials, discounts or dynamic pricing, but again these should be known variables. In the event that prices are raised in the future, the LTV model will be updated accordingly to reflect the higher price point.
Renewal probability is an unknown variable that is estimated using the FT’s predictive model for both B2C and B2B customers. Given that we will never be certain whether a customer will renew their subscription or not, renewal probability draws on habits that are positively correlated with customer loyalty. There are two aspects considered in its calculation:
- “Love” - How much does the customer value their subscription?
- “Money” - How much disposable income or wealth does the customer have?
Given that these factors cannot be observed directly, the FT uses proxies for love, money and both. The following shows the breakdown of those proxies.
- Proxies for love - Engagement (measured through the FT’s RFV model)
- Proxies for money - Job position (data collected during the registration process), region, payment method, number of payment failures, subscription discount
- Proxies for both - Number of previous renewals, renewal frequency (i.e. monthly or annually), product (i.e. trial, standard or premium), whether they have trialled previously
LTV of B2C FT subscribers is most impacted by an individual’s engagement levels, while discounting has a relatively low influence
The FT’s predictive model allows you to assign scores to each major factor based on its likelihood to impact renewal probabilities. This is a critical step as some factors may hold more weight than others, even if they are all statistically significant. Broadly speaking, the two most important factors that result in a higher B2C LTV are engagement and number of previous renewals. On the other hand, the amount of discounting they have received only matters a small amount.
LTV of B2B accounts is also impacted by engagement, but it harder to predict due to the way organisational pricing works
As with B2C LTV, the B2B segment is calculated by looking at the contract price and likelihood of renewal. However, there is less certainty as the price can grow or shrink depending on the number of seats in the contract. This is because the FT uses a unique engagement-based pricing where clients have access to unlimited licences within the organisation but are charged based on active users as opposed to having a set number of logins available.
In terms of renewal probability, the most important variables largely match that of B2C. The most critical factor is the proportion of engaged readers. We believe this is because the more engaged users are, the more value they are deriving from the product. The next most important feature is the total number of users on the licence. This is due to the fact that large contracts have the ability to remove seats, as opposed to cancelling the contract outright. The other factors are less important but still statistically significant and are leveraged accordingly.
FT Strategies can help you adopt this valuable metric
Overall, customer LTV is an incredibly vital metric that can be used to track and optimise for your organisation’s long term financial success.
Hopefully this article will help serve as a useful guide to calculating customer LTV for the first time. At FT Strategies, we’ve helped many businesses use metrics like LTV to build engaged audiences, increase the value their customer receives as well as the long term returns for the company. For more information, feel free to reach out directly to [email protected]
About the author
Liat is a Senior Consultant with several years experience working in consulting, technology, and venture capital in Canada, Southeast Asia, and the UK. Prior to joining FT Strategies, Liat served as a Management Consultant at Bain in the Toronto and London offices where she has worked on €500M+ org restructuring, digital transformations, and several private equity deals. She is a Toronto native who is fascinated with media polarisation and mis/disinformation.