Calculate Customer Lifetime Value | Python Tutorial

Follow this easy and quick tutorial to calculate customer lifetime value for your customer records. This is one of the most important metrics in business. You can establish how to calculate this with ease using some simple Python code.
Find the dataset here:
absentdata.com...
Contact me on linkedin:
/ gaelimholland
0:30 Agenda
0:50 Data Import and Preparation
4:14 Create Customer Grouping Function
5:45 Elements of the CLTV Function
8:40 Apply CLTV Equations.
#dataanalysis
#datascience
#marketing
#ecommerce

[@ronenTheBarbarian]

Great vid Absent Data! Reminds me a bit of the RFM analysis you did in the past. Super helpful as always!

[@absentdata]

Yes, RFM is the basis for the alot of CLTV calculations. Great observations. I am glad you like the video!

[@ahmedshalaby9343]

man cheers from egypt your awesome

[@ahsanshafiqchaudhry]

1- max_date remains constant so how come "max_date-x.min()" while grouping by customer would give you total age of customer? max_date is not last date of purchase for customer. 2- The date columns you have calculated is in fact the time period between first transaction of customer and the 12-09-2011. Good job though

[@mohamed.montaser]

can you share the github link for this and the dataset please?

[@absentdata]

sure its the ecommerce dataset on this page: absentdata.com/data-analysis/where-to-find-data/