Customer Analytics: Lifetime Value

Customer Lifetime Value (CLV) is an often over used and over simplified term people use to describe the amount of value a customer generates for your business.  Take this example from Kissmetrics where they average together “expenditure” and “visits” into their variables and then take a random retention rate (r) to calculate the LTV.  From this simple example, they come up with a range of $5k – $25k ??? that’s over a 5X difference and then they average it together??!?! Yikes!

bad ltv

kiss metrics

When I first joined AVG Technologies back in 2010 a customer life time value estimate was generated in a very similar way by simply dividing the total annual revenue by the monthly active users.  Unfortunately, this was a bogus metric as it really over simplified customer lifetime, monetization, and the true cost of acquisition.  Especially considering the fact we had over 110 Million users worldwide.  At its best, the CLV answers in one simple number all of the most important questions about your customers:

Where do my customers come from? How many are making their way through the acquisition and on-boarding funnel to become an active user? and at what cost?

What is the average lifetime of my customers? What impacts their churn behavior and what dimensions are important to segment by?

How many transactions are conducted in the lifetime of my customer? what is the average order value?

Over time, we developed a methodology to extract clickstream cohorts with the channel attribution information and join it to the customer record data.  We then conducted linear regression over a multitude of dimensions to identify the key variables that impact the churn or monetization of a customer.  For more information, please contact me directly @whatisanalytics

At its worse, a bad CLV can lead to over spending on acquiring users; a death wish in the startup space or underestimating the total value a marketing campaign or new product introduction could be generating.

Other great references to look at:


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