One particular solution is to stop reporting CLV as a single number. I mean, if you happen to know that there are two disjoint sets of Good Customers and Bad Customers than that is very useful information to anyone who needs to know the CLV number to do their job. "What can we afford to spend to acquire a new customer? I have a new channel I want to try." "It really depends on whether you're getting Good Customers or Bad Customers. We can only pay $50 for BCs, but for GCs we can go $200+."
You then get into issues like "How do I tell the difference between a Bad Customer and a Good Customer at an equivalent vintage where neither have churned yet?" It may be the case that there are behaviors which you can use as a proxy for which group someone is likely to fall in. Dharmesh Shah talks often about a Customer Happiness Index that Hubspot uses, which is essentially a regression that predicts churn rate based on measurable customer behavior. It's all sorts of win to find that something like that works for your business. (Hypothetical example: if Dropbox found that customers who used photo sharing are the best possible Dropbox customers, it would make sense to test things like a) biasing marketing to target photo sharers or b) bias product design to push photo sharing as a feature.)