For more meaningful metrics, you’ll need more data than just your LTV.  You need to be able to properly separate customers with High LTV’s from customers with Low LTV’s. To do this, simply add a “group by” & “order by” to the above SQL statement:

That covers how to get a granular look at your customers & their lifetime value. But now that you’ve got that, you’re probably going to want to see where the bad customers came from. Conversely, you might have some intuition that certain traffic has a lower or higher LTV than others. Basically, you’ll want to go deeper.

If you’d like to segment your customers by traffic source/medium/campaign, you need to get that data somehow & analytics like Google/Clicky don’t have granular data export (each visitors ip/browser/os) in their API. So, you will need to collect that data & somehow pin it to customers record at the time of signup. You might have to update your database/store to accomplish this. It’s actually really simple to do, just grab the url params & pass them through to the sign up page. Here are a couple of examples…

in Python (django):

in PHP:

Once you do that, things get fun; you can:

1. Mash up data from google analytics with your own data by using their API [link].
2. Segment your customers by sex, age group, education, marital status, etc using the Rapleaf API [link]
3. See if customers in certain cities or states have higher LTV (using SQL)

With the above information, you can create dashboards for realtime campaign tracking (or just dashboard widgets, like one for geckoboard [link]), daily emails with metrics, create upsells to your customers based on LTV (in order to raise low LTV or increase revenue in high LTV customers), re-focus your marketing/pr efforts where they will make the most impact based on historic data & if you’re really nerdy (& smart) run ML/Pattern recognition on your data to find patterns that may be interesting.