In past article we have disentangled the potential pitfalls of a marketing strategy based solely on minimizing budget and spending to acquire new customers across several channels, and the answer to this problem lies in the adoption of a Lifetime Value approach that best fits the company’s needs. Fastest one for computing Customer Lifetime Value is Historical CLV, which computes a customer‘s lifetime value based on what they have previously spent with the company. There are 2 main methods for calculating historical CLV, one using Average Revenue Per User (ARPU) and the other using Cohort Analysis. However, I would like to mention first some points associated with taking a backward-looking approach to CLV analysis.
Main downside of historical CLVs is that it does not account for time; meaning that it puts all of your customers, new and old, into the same group when they may actually behave substantially different. For instance this applies in cases whereby you launch some new service/product lines or drastically shift your advertising channels and this results in new customers with different features and behaviors than your existing ones, differences that imply changing CLVs fundamentals as well. In addition to this, you should never compute CLV as (Total Revenue) ÷ (Total Customers), as this would actually ignore the fact that some customers have been with you already for much longer time.
To calculate historical CLV by means of ARPU technique, you simply need to calculate the Average revenue per customer per month as (Total Revenue) ÷ (# months since customer joined), add them up, and finally multiply by 12 or 24 to get a 1-yr or 2-yrs CLV.
Let’s imagine that Alex and Rob are your only 2 customers and their purchases at company XY are the following:
If today is 1st July 2013, then your average monthly revenue from Alex is ($15 + $5 + $10)/6 = $5 and your average monthly revenue from Rob is ($4.5 + $7.5 + $10)/2 = $11. Adding these 2 numbers gives you an average monthly revenue per customer of ($16/2) = $8. In order to find a 12m or 24m CLV, then multiply those numbers by 12 or 24.
As you can notice, advantage of ARPU is clearly its simplicity, while on the other hand main drawback is that the overall figure can actually be unreliable if you have a lot of new or a lot of old customers; furthermore, it fails in signalling changes in customers’ behaviors. For instance if you had previously managed to acquire a lot of Alex type of customers but a new Display-Ad is making your company very popular among Rob type of customers, then your average monthly revenue per customer in the future is likely to be closer to Rob’s than Alex’s lower one.
Next session we will discover how Cohort analysis takes the ARPU approach one step further.
Image source: Flickr