Churn is basically the impact of losing clients (or contracts), expressed either in terms of the number of clients lost – “client churn” – or lost revenues as a result of lost clients – “revenue churn”. Downsell is defined as the diminution of revenue on existing clients (or contracts). The concept of “churn” may sometimes implicitly include downsell. Additionally, a distinction can be made between “gross” and “net” churn, the latter encompassing the offsetting impact of upselling effects (upgrades, expansion, etc.). Here, we will focus our analysis on “gross” churn, emphasising the impact of client losses (with an analysis that applies to downsell in most instances).
Although churn is referred to as a sort of “negative” force, it is not necessarily bad in itself. For instance, it can result from a company’s voluntary strategy to focus on certain categories of clients or a specific type of growth. Still, understanding the way it works and how to measure it appropriately definitely contributes to improved business and investment decisions.
I. Formulating the most relevant definitions
Though freedom and creativity are permitted in setting the methodology for calculating churn, analyzing churn is all the more relevant when (i) revenue is recurring by nature, (ii) the loss is addressed on a client basis rather than on a contract or project basis and (iii) the primary focus is on the loss expressed in terms of value.
- Recurring vs. non-recurring revenue
Determining churn is mostly useful in business models dominated by recurring clients and/or recurring contracts, which is typically the case in SaaS subscription-based models. In such cases, as revenues per client/contract are supposed not to be reversed over time, losing a client in particular can be considered as permanent (and not offset a few months later by new recurring revenues from the same client). In the case of an interruption of revenue with a client for say a few months, the notion of “churn” should be replaced by alternative concepts (downsell, contraction…). In SaaS models, the non-recurring part (professional services: integration, training…; proof of concepts etc.) of revenues should then be excluded from the calculation.
In some other business models, the frontier between recurring and non-recurring revenues is not that obvious. Some clients may indeed contribute to revenues every year, and therefore be considered “recurring” clients, while in the meantime their contribution levels are relatively variable. In such cases, it can still make sense to monitor churn, though its calculation should be adapted accordingly (depending on the variance of revenues, a “volume” approach – client churn – is preferred).
2. Revenue per client vs. revenue per contract/project
We believe that churn calculation, particularly in a SaaS environment, should be performed on a client basis rather than a contract or project basis. Indeed, the loss of a client is not as easily reversible and less complicated to identify, whereas the loss of a contract with an existing client may be offset by the signing of new contracts. Also, owing to the complexity of the following up on multiple contracts per client, we often observe that it is not possible to precisely ascertain whether a new contract can be considered as the replacement of another that is ending, or as a brand new contract with a completely different offer with no overlap. However, we’re not saying that monitoring churn by contract or project is not useful. With all the required details, it provides interesting information about the dynamics of the evolution of the offer per client, and is particularly well suited for scrutinising dowsell drivers.
3. Value vs. volume-based approaches
The churn rate can be calculated uising different methodologies. One of the most basic, a “volume-based” approach, is to measure the ratio of the number of clients lost over a certain period of time relative to the number of clients at the beginning of the period (“client churn”). Note that the period under consideration should not be so long as to skew the calculation by losses related to new clients acquired during the period. While this approach is interesting for analysing a company’s capacity to retain its clients, it does not provide any information regarding the real impact of churn on revenue. Indeed, a company can lose a significant proportion of its clients while still maintaining a strong base of recurring revenue. That is why the priority approach, especially in a SaaS-based environment, should be to focus on revenues expressed in monetary units.
From a financial analysis perspective, the churn rate calculation derived from lost recurring revenues is the most interesting one. In SaaS models with monthly recurring revenue (MRR), it involves measuring the ratio between the MRR at the end of a period and the MRR at the beginning of the period (the yearly rate expressed over a 12-month period is the most common). Note that this approach, when combined with the previous volume-based one, provides useful information on loss concentration.
Below is an illustration of such a calculation, both on a monthly and yearly basis – only available in (n+1) for the latter.
In the above example, it is particularly interesting to see that yearly churn rates can vary considerably from month to month. With such fluctuations, the life-time value (LTV), derived from a “normative churn rate”, should be carefully assessed. Where there is too much discomfort regarding the normative level of the churn rate, we consider that any LTV calculation should be deemed irrelevant (an alternative approach would be to provide information in terms of ranges).
II. Deciphering churn through appropriate angles:
Now that the basic definitions have been clarified, let’s get into more detail about the analytical approaches within the framework of a subscription-based model, where churn expressed in terms of clients and recurring revenue. Note that the following approaches can (should) be combined.
- Size of clients
When broken down by size ranges expressed in terms of recurring revenue (MRR), client churn analysis helps to better understand a company’s strategy and/or the influence of the market. We often observe that churn is more concentrated on lower MRR per client, which can primarily be explained by (i) an offer less adapted to smaller clients or (ii) a company not sufficiently focusing on what are considered to be less profitable clients. On the contrary, even if less intuitive, a company may sometimes choose to focus more on small or medium-sized clients, with larger ones being considered not profitable enough in the context of either less favourable pricing power and/or too complex (and costly) solutions to be delivered.
2. Type of offers and products
Each loss of a client (or contract) can be linked to a dominant type of offer or product. By carefully analysing churn, a company can better understand which offer(s) to focus on. To take it further, in addition to the type of offer, it may prove particularly interesting to analyse the funnel of acquisition (for paid client) and determine for instance whether a client who first experienced a freemium or trial offer is less prone to churn.
3. Geography
Some clients from a particular geographic area, irrespective of their size or the type of offer they have subscribed to, may be more inclined to churn, notably due to certain local conditions, for instance, competition, regulation or the economic environment.
4. Vintages
By determining cohorts based on the year the client was acquired (vintage cohorts), we can identify whether churn is more related to clients, gained in specific years, as well as calculate the different churn ratios over the lifetime of a contractual relationship. To illustrate, in the latter case, churn rates can (i) be more important in the early years, when some clients have sufficiently exhausted the basic features of the services provided by the company and are evolving towards other, more complex alternative solutions, and (ii) decrease when the client base now consists of more loyal clients.
5. Reasons
The classification of churn by client size, type of offers, product, geography or vintage, provides a particularly interesting analytical framework for identifying the underlying reasons for churn. More explicitly, we recommend investigating client churn whenever it occurs (with more diligence when the loss of clients is more significant – note that some companies provide a specific form in which they ask each churned client to clarify their motivation for cancelling) and classifying them according to relevant categories, including: (i) better offer from competitors (price, package…), (ii) a solution that is not reliable enough, (iii) poor customer service, (iv) a solution that is no longer useful, (v) the client is in financial difficulties or (vi) financial consolidation within a new group (the last two are often highlighted in some companies’ communication to highlight the quality of the retention policy).
Below is a summary of the different definitions, approaches etc., which we propose in our article:
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