The Maximum Viable Churn Rate for a Startup
An entrepreneur asked me the question, what is the maximum viable churn for a startup? Within that question, a few others are embedded. How should a founder think about trading off efforts to grow revenue and mitigate churn? What is the impact of account growth on net churn? Startups must walk a tight-rope to balance growth, churn and cash. Below is the framework I use for working through maximum viable churn.
Startups with higher churn chew through more capital to maintain the same growth rate. Let’s compare four scenarios of a SaaS company with $1000 annual customer revenue, a 0.8 sales efficiency metric, implied cost-of-customer-acquisition of $1250 and 3,000 customers generating $3M in annual revenue.
|Annual Churn||22%||31%||39 %||46%|
|CAC Spend to Recoup in $M||0.8||1.2||1.5 ||1.7 |
The greater the churn, the more capital is required for the business just to maintain its revenue. In the case of the 5% monthly churn, the business would need to reinvest 60% of its total revenues, $1.7M, this year to keep revenue flat from year to year. The 2% monthly churn scenario requires less-than-half the capital. This makes sense because churn is a measure of how efficient a company is at retaining customer revenue.
Since churn is directly tied to burn rate and the startup’s runway, the maximum viable churn rate for a business is the churn rate that enables the company to grow the fastest while raising the next round of capital at an attractive valuation. To me, that means targeting 18 to 24 months of runway post-Series A.
Let’s take a hypothetical SaaS company at a $2.5M annual run rate which raises a $5M Series A at 10x its ARR for a $25M post. Assuming the company would like to raise the next round at $50M post, the company would need to double its revenue. A 5% monthly churn rate wouldn’t work because the company would need to invest about $1.7M to sustain its revenues and another $3.75M to double its customers, totaling more than the $5M raised just for customer acquisition. At 1% monthly churn, the company could invest $0.8M to maintain its revenue and another $3.75M to add another 3000 customers, for a total investment of $4.6M, and within budget of the raise, ignoring for a moment the other costs of the startup.
In practice, churn rates vary by customer segment. Startups serving SMBs tend to operate with higher monthly churn, somewhere between 2.5% and 5%+, because SMBs go out of business with greater frequency and tend to be acquired and managed through less retentive channels, e.g. self-service. In the mid-market, which I’d define by average customer revenue of between $10k and $250k loosely speaking, the churn rates I’ve seen are between 1% and 2% per month. Enterprise companies, those with customers paying more than $250k per year are typically closer to 1%.
As the spend per customer grows, startups can afford to invest significantly more in retaining the customer
, hence the improving rates.
The other dynamic at play that isn’t immediately apparent is the growth of customer accounts. In the mid-market and the enterprise, account growth from cross-sales and up-sales are common. Best-in-class companies are able to grow accounts by 3% to 4% per month. This account growth offsets churn. A company churning accounts at 3% but growing existing customers at 3% will have a net 1.2% annualized revenue churn, compared to 31% without account growth. Because retaining existing customers is less expensive than finding new ones, investing in customer success to grow accounts is a far more cost- effective way
of mitigating churn than increasing customer acquisition spend.
The maximum viable churn for a company depends on the company’s runway and the rate at which the startup can grow accounts through up-sell and cross-sell. It goes without saying that less churn is always better, but estimating an upper-bound for churn can be helpful for financial modeling and internal prioritization of customer success efforts.
Published 2014-01-29 in
Tomasz Tunguz is partner at Redpoint
. I write daily, data-driven blog posts about key questions facing startups. I co-authored the
book, Winning with Data
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