Nice, thanks for sharing your knowledge! What are your thoughts/experience with predicted CLV and using that to justify CAC? Especially in leadgen b2b, the feedback loop of $ is quite long and payback period as well. However, total LTV is often very large. Do you try to move to pCLV to justify high CAC?
Thanks Rick! funny i posted about this on Linkedin today haha (https://www.linkedin.com/posts/activity-7292487105849516032-2sIa?utm_source=share&utm_medium=member_desktop) pCLV is sometimes (most times) built on incomplete or messy data, which means businesses end up making decisions based on models that don’t hold up to real world conditions. In B2B, it’s definitely trickier. I normally advise to take a more pragmatic approach when using pCLV to justify CAC. For ex, instead of looking at total lifetime value, which can be pretty abstract, focus on stuff like the value generated in the first 6–12 months (12-24, etc). It’s a much more practical way to align CAC with the cash flow you can realistically expect within that timeframe rather than banking on total CLV value.
This also makes u overstimate less when u rely on aggregate CLV. Also segmenting by deal size, industry, or product type often reveals patterns that can make predictions more reliable.
pCLV just as data collection is only as good as the system it’s built on, it’s either a risk or justification.
Balancing customer acquisition and retention can be tricky, especially when CLV feels like a moving target. You could try using mailsAI to get clearer insights on customer lifetime value and optimize your media spend accordingly. That approach helped me see beyond just ROAS and focus on real profitability.
Love the post. Lots of great insights to digest in here. Thank you so much!
Thank you for taking the time to read it!
Nice, thanks for sharing your knowledge! What are your thoughts/experience with predicted CLV and using that to justify CAC? Especially in leadgen b2b, the feedback loop of $ is quite long and payback period as well. However, total LTV is often very large. Do you try to move to pCLV to justify high CAC?
Thanks Rick! funny i posted about this on Linkedin today haha (https://www.linkedin.com/posts/activity-7292487105849516032-2sIa?utm_source=share&utm_medium=member_desktop) pCLV is sometimes (most times) built on incomplete or messy data, which means businesses end up making decisions based on models that don’t hold up to real world conditions. In B2B, it’s definitely trickier. I normally advise to take a more pragmatic approach when using pCLV to justify CAC. For ex, instead of looking at total lifetime value, which can be pretty abstract, focus on stuff like the value generated in the first 6–12 months (12-24, etc). It’s a much more practical way to align CAC with the cash flow you can realistically expect within that timeframe rather than banking on total CLV value.
This also makes u overstimate less when u rely on aggregate CLV. Also segmenting by deal size, industry, or product type often reveals patterns that can make predictions more reliable.
pCLV just as data collection is only as good as the system it’s built on, it’s either a risk or justification.
Balancing customer acquisition and retention can be tricky, especially when CLV feels like a moving target. You could try using mailsAI to get clearer insights on customer lifetime value and optimize your media spend accordingly. That approach helped me see beyond just ROAS and focus on real profitability.